Thomas Berger

We report on a search for weakly interacting massive particles (WIMPs) using 278.8 days of data collected with the XENON1T experiment at LNGS. XENON1T utilizes a liquid xenon time projection chamber with a fiducial mass of (1.30±0.01) ton, resulting in a 1.0 ton yr exposure. The energy region of interest, [1.4,10.6] keV_{ee} ([4.9,40.9] keV_{nr}), exhibits an ultralow electron recoil background rate of [82_{-3}^{+5}(syst)±3(stat)] events/(ton yr keV_{ee}). No significant excess over background is found, and a profile likelihood analysis parametrized in spatial and energy dimensions excludes new parameter space for the WIMP-nucleon spin-independent elastic scatter cross section for WIMP masses above 6 GeV/c^{2}, with a minimum of 4.1×10^{-47} cm^{2} at 30 GeV/c^{2} and a 90% confidence level.

We report the first dark matter search results from XENON1T, a ∼2000-kg-target-mass dual-phase (liquid-gas) xenon time projection chamber in operation at the Laboratori Nazionali del Gran Sasso in Italy and the first ton-scale detector of this kind. The blinded search used 34.2 live days of data acquired between November 2016 and January 2017. Inside the (1042±12)-kg fiducial mass and in the [5,40] keV_{nr} energy range of interest for weakly interacting massive particle (WIMP) dark matter searches, the electronic recoil background was (1.93±0.25)×10^{-4} events/(kg×day×keV_{ee}), the lowest ever achieved in such a dark matter detector. A profile likelihood analysis shows that the data are consistent with the background-only hypothesis. We derive the most stringent exclusion limits on the spin-independent WIMP-nucleon interaction cross section for WIMP masses above 10 GeV/c^{2}, with a minimum of 7.7×10^{-47} cm^{2} for 35-GeV/c^{2} WIMPs at 90% C.L.

This paper presents a spatial multi-agent programming model, which has been developed for assessing policy options in the diffusion of innovations and resource use changes. Unlike conventional simulation tools used in agricultural economics, the model class described here applies a multi-agent/cellular automata (CA) approach by using heterogeneous farm-household models and capturing their social and spatial interactions explicitly. The individual choice of the farm-household among available production, consumption, investment and marketing alternatives is represented in recursive linear programming models. Adoption constraints are introduced in form of network-threshold values that reflect the cumulative effects of experience and observation of peers’ experiences. The model's economic and hydrologic components are tightly connected into a spatial framework. The integration of economic and hydrologic processes facilitates the consideration of feedback effects in the use of water for irrigation. The simulation runs of the model are carried out with an empirical data set, which has been derived from various data sources on an agricultural region in Chile. Simulation results show that agent-based spatial modelling constitutes a powerful approach to better understanding processes of innovation and resource use change.

DARk matter WImp search with liquid xenoN (DARWIN) will be an experiment for the direct detection of dark matter using a multi-ton liquid xenon time projection chamber at its core. Its primary goal will be to explore the experimentally accessible parameter space for Weakly Interacting Massive Particles (WIMPs) in a wide mass-range, until neutrino interactions with the target become an irreducible background. The prompt scintillation light and the charge signals induced by particle interactions in the xenon will be observed by VUV sensitive, ultra-low background photosensors. Besides its excellent sensitivity to WIMPs above a mass of 5 GeV/c2, such a detector with its large mass, low-energy threshold and ultra-low background level will also be sensitive to other rare interactions. It will search for solar axions, galactic axion-like particles and the neutrinoless double-beta decay of 136Xe, as well as measure the low-energy solar neutrino flux with < 1% precision, observe coherent neutrino-nucleus interactions, and detect galactic supernovae. We present the concept of the DARWIN detector and discuss its physics reach, the main sources of backgrounds and the ongoing detector design and R&D efforts.

We report constraints on light dark matter (DM) models using ionization signals in the XENON1T experiment. We mitigate backgrounds with strong event selections, rather than requiring a scintillation signal, leaving an effective exposure of (22±3) tonne day. Above ∼0.4 keVee, we observe <1 event/(tonne day keVee), which is more than 1000 times lower than in similar searches with other detectors. Despite observing a higher rate at lower energies, no DM or CEvNS detection may be claimed because we cannot model all of our backgrounds. We thus exclude new regions in the parameter spaces for DM-nucleus scattering for DM masses mχ within 3–6 GeV/c2, DM-electron scattering for mχ>30 MeV/c2, and absorption of dark photons and axionlike particles for mχ within 0.186–1 keV/c2.Received 29 July 2019Revised 7 November 2019DOI:https://doi.org/10.1103/PhysRevLett.123.251801Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDark matterParticle dark matterPhysical SystemsWeakly interacting massive particlesTechniquesDark matter detectorsTime-projection chambersGravitation, Cosmology & AstrophysicsParticles & Fields

We report results from searches for new physics with low-energy electronic recoil data recorded with the XENON1T detector. With an exposure of 0.65 tonne-years and an unprecedentedly low background rate of 76±2stat events/(tonne×year×keV) between 1 and 30 keV, the data enable one of the most sensitive searches for solar axions, an enhanced neutrino magnetic moment using solar neutrinos, and bosonic dark matter. An excess over known backgrounds is observed at low energies and most prominent between 2 and 3 keV. The solar axion model has a 3.4σ significance, and a three-dimensional 90% confidence surface is reported for axion couplings to electrons, photons, and nucleons. This surface is inscribed in the cuboid defined by gae<3.8×10−12, gaegeffan<4.8×10−18, and gaegaγ<7.7×10−22 GeV−1, and excludes either gae=0 or gaegaγ=gaegeffan=0. The neutrino magnetic moment signal is similarly favored over background at 3.2σ, and a confidence interval of μν∈(1.4,2.9)×10−11 μB (90% C.L.) is reported. Both results are in strong tension with stellar constraints. The excess can also be explained by β decays of tritium at 3.2σ significance with a corresponding tritium concentration in xenon of (6.2±2.0)×10−25 mol/mol. Such a trace amount can neither be confirmed nor excluded with current knowledge of its production and reduction mechanisms. The significances of the solar axion and neutrino magnetic moment hypotheses are decreased to 2.0σ and 0.9σ, respectively, if an unconstrained tritium component is included in the fitting. With respect to bosonic dark matter, the excess favors a monoenergetic peak at (2.3±0.2) keV (68% C.L.) with a 3.0σ global (4.0σ local) significance over background. This analysis sets the most restrictive direct constraints to date on pseudoscalar and vector bosonic dark matter for most masses between 1 and 210 keV/c2. We also consider the possibility that 37Ar may be present in the detector, yielding a 2.82 keV peak from electron capture. Contrary to tritium, the 37Ar concentration can be tightly constrained and is found to be negligible.8 MoreReceived 30 June 2020Accepted 16 September 2020DOI:https://doi.org/10.1103/PhysRevD.102.072004Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAxionsBeta decayMagnetic momentParticle astrophysicsParticle dark matterSolar neutrinosTechniquesDark matter detectorsMulti-purpose particle detectorsParticles & FieldsNuclear Physics

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

The XENON1T experiment is currently in the commissioning phase at the Laboratori Nazionali del Gran Sasso, Italy. In this article we study the experiment's expected sensitivity to the spin-independent WIMP-nucleon interaction cross section, based on Monte Carlo predictions of the electronic and nuclear recoil backgrounds. The total electronic recoil background in $1$ tonne fiducial volume and ($1$, $12$) keV electronic recoil equivalent energy region, before applying any selection to discriminate between electronic and nuclear recoils, is $(1.80 \pm 0.15) \cdot 10^{-4}$ ($\rm{kg} \cdot day \cdot keV)^{-1}$, mainly due to the decay of $^{222}\rm{Rn}$ daughters inside the xenon target. The nuclear recoil background in the corresponding nuclear recoil equivalent energy region ($4$, $50$) keV, is composed of $(0.6 \pm 0.1)$ ($\rm{t} \cdot y)^{-1}$ from radiogenic neutrons, $(1.8 \pm 0.3) \cdot 10^{-2}$ ($\rm{t} \cdot y)^{-1}$ from coherent scattering of neutrinos, and less than $0.01$ ($\rm{t} \cdot y)^{-1}$ from muon-induced neutrons. The sensitivity of XENON1T is calculated with the Profile Likelihood Ratio method, after converting the deposited energy of electronic and nuclear recoils into the scintillation and ionization signals seen in the detector. We take into account the systematic uncertainties on the photon and electron emission model, and on the estimation of the backgrounds, treated as nuisance parameters. The main contribution comes from the relative scintillation efficiency $\mathcal{L}_\mathrm{eff}$, which affects both the signal from WIMPs and the nuclear recoil backgrounds. After a $2$ y measurement in $1$ t fiducial volume, the sensitivity reaches a minimum cross section of $1.6 \cdot 10^{-47}$ cm$^2$ at m$_χ$=$50$ GeV/$c^2$.

This paper describes an agent-based software package, called Mathematical Programming-based Multi Agent Systems (MP-MAS), which builds on a tradition of using constrained optimization to simulate farm decision-making in agricultural systems. The purpose of MP-MAS is to understand how agricultural technology, market dynamics, environmental change, and policy intervention affect a heterogeneous population of farm households and the agro-ecological resources these households command. The software is presented using the Overview, Design concepts, and Details (ODD) protocol. Modeling features are demonstrated with empirical applications to study sites in Chile, Germany, Ghana, Thailand, Uganda, and Vietnam. We compare MP-MAS with eight other simulators of human–environment interactions (ABSTRACT, CATCHSCAPE, ECECMOD, IMT, LUDAS, PALM, SAM, and SIM). The comparison shows that the uniqueness of MP-MAS lies in its combination of a microeconomic modeling approach and a choice of alternative biophysical modules that are either coded as part of the software or coupled with it using the Typed Data Transfer (TDT) library.

The XENON1T experiment at the Laboratori Nazionali del Gran Sasso (LNGS) is the first WIMP dark matter detector operating with a liquid xenon target mass above the ton-scale. Out of its 3.2 t liquid xenon inventory, 2.0 t constitute the active target of the dual-phase time projection chamber. The scintillation and ionization signals from particle interactions are detected with low-background photomultipliers. This article describes the XENON1T instrument and its subsystems as well as strategies to achieve an unprecedented low background level. First results on the detector response and the performance of the subsystems are also presented.

XENONnT is a dark matter direct detection experiment, utilizing 5.9 t of instrumented liquid xenon, located at the INFN Laboratori Nazionali del Gran Sasso. In this work, we predict the experimental background and project the sensitivity of XENONnT to the detection of weakly interacting massive particles (WIMPs). The expected average differential background rate in the energy region of interest, corresponding to (1, 13) keV and (4, 50) keV for electronic and nuclear recoils, amounts to 12.3 ± 0.6 (keV t y) -1 and (2.2± 0.5)× 10 −3 (keV t y) -1 , respectively, in a 4 t fiducial mass. We compute unified confidence intervals using the profile construction method, in order to ensure proper coverage. With the exposure goal of 20 t y, the expected sensitivity to spin-independent WIMP-nucleon interactions reaches a cross-section of 1.4×10 −48 cm 2 for a 50 GeV/c 2 mass WIMP at 90% confidence level, more than one order of magnitude beyond the current best limit, set by XENON1T . In addition, we show that for a 50 GeV/c 2 WIMP with cross-sections above 2.6×10 −48 cm 2 (5.0×10 −48 cm 2 ) the median XENONnT discovery significance exceeds 3σ (5σ). The expected sensitivity to the spin-dependent WIMP coupling to neutrons (protons) reaches 2.2×10 −43 cm 2 (6.0×10 −42 cm 2 ).

We report the first experimental results on spin-dependent elastic weakly interacting massive particle (WIMP) nucleon scattering from the XENON1T dark matter search experiment. The analysis uses the full ton year exposure of XENON1T to constrain the spin-dependent proton-only and neutron-only cases. No significant signal excess is observed, and a profile likelihood ratio analysis is used to set exclusion limits on the WIMP-nucleon interactions. This includes the most stringent constraint to date on the WIMP-neutron cross section, with a minimum of $6.3\times10^{-42}$ cm$^2$ at 30 GeV/c${}^2$ and 90% confidence level. The results are compared with those from collider searches and used to exclude new parameter space in an isoscalar theory with an axial-vector mediator.

Many developing countries grapple with high rates of farmland degradation and low agricultural productivity amidst increasing climate variability. Considerable efforts have been exerted to promote the diffusion of improved farmland management to address these challenges. Despite these efforts, adoption rates, especially of soil conservation and water harvesting technologies, are still low, which has been the subject of investigation in several studies in Ethiopia and elsewhere. Most studies on the adoption of these technologies, however, tend to focus on economic incentives only, paying little attention to the role of social capital. This article provides evidence of the effects of different dimensions of social capital on innovation adoption across households holding different levels of risk aversion. We address this issue by using cross section and panel data from Ethiopia. Results show that social capital plays a significant role in enhancing the adoption of improved farmland management practices. We also find evidence that the effect of social capital across households with heterogeneous risk taking behavior is different.

Direct dark matter detection experiments based on a liquid xenon target are leading the search for dark matter particles with masses above $\ensuremath{\sim}5\text{ }\text{ }\mathrm{GeV}/{c}^{2}$, but have limited sensitivity to lighter masses because of the small momentum transfer in dark matter-nucleus elastic scattering. However, there is an irreducible contribution from inelastic processes accompanying the elastic scattering, which leads to the excitation and ionization of the recoiling atom (the Migdal effect) or the emission of a bremsstrahlung photon. In this Letter, we report on a probe of low-mass dark matter with masses down to about $85\text{ }\text{ }\mathrm{MeV}/{c}^{2}$ by looking for electronic recoils induced by the Migdal effect and bremsstrahlung using data from the XENON1T experiment. Besides the approach of detecting both scintillation and ionization signals, we exploit an approach that uses ionization signals only, which allows for a lower detection threshold. This analysis significantly enhances the sensitivity of XENON1T to light dark matter previously beyond its reach.

By combining crop, livestock and/or forestry activities in the same area, integrated systems (IS) can increase organic matter content in the soil – which favors biomass production and allows for higher livestock stocking rates in pasturelands. The implementation of IS is therefore seen as a promising strategy for sustainable agricultural intensification in Brazil, particularly in Mato Grosso state (MT). However, despite the benefits associated with IS and incentives offered by the federal government to stimulate their dissemination, little is known about these systems or the challenges to implement them, and only a limited number of farmers have adopted IS so far. This paper presents a comprehensive assessment of all IS identified in Mato Grosso by 2012/13, which were mapped and described in terms of their main technical and non-technical features. These findings were combined with farm survey data set to provide a detailed account of the various technologies currently being disseminated, their individual diffusion levels and potential adoption constraints. Results generated through qualitative and quantitative research methods give an overview of IS’ state of the art, reveal farmer perception of such technology and offer insights into the prospects for low-carbon agriculture in the region. The study’s major findings are that IS are present in more than 40 of the 141 municipalities of MT, and the vast majority (89%) involve only crop and livestock. Farmers have adopted three different crop–livestock configurations, depending on their production strategy. Cultural aspects play a major role in farmer decisions to adopt IS, credit provision has not been relevant for IS adoption, and a broader dissemination of IS may occur as land transitions continue.

Abstract Computer simulation models can provide valuable insights for climate‐related analysis and help streamline policy interventions for improved adaptation and mitigation in agriculture. Computable general equilibrium ( CGE ) and partial equilibrium ( PE ) models are currently being expanded to include land‐use change and energy markets so that the effects of various policy measures on agricultural production can be assessed. Agent‐based modelling ( ABM ) or multi‐agent systems ( MAS ) have been suggested as a complementary tool for assessing farmer responses to climate change in agriculture and how these are affected by policies. MAS applied to agricultural systems draw on techniques used for R ecursive F arm P rogramming, but include models of all individual farms, their spatial interactions and the natural environment. In this article, we discuss the specific insights MAS provide for developing robust policies and land‐use strategies in response to climate change. We show that MAS are well‐suited for uncertainty analysis and can thereby complement existing simulation approaches to advance the understanding and implementation of effective climate‐related policies in agriculture.

The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers' decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers' decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers' behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers' decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers' emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.

We report on WIMP search results of the XENON100 experiment, combining three runs summing up to 477 live days from January 2010 to January 2014.Data from the first two runs were already published.A blind analysis was applied to the last run recorded between April 2013 and January 2014 prior to combining the results.The ultralow electromagnetic background of the experiment, ∼5 × 10 -3 events=ðkeV ee × kg × dayÞ) before electronic recoil rejection, together with the increased exposure of 48 kg × yr, improves the sensitivity.A profile likelihood analysis using an energy range of ð6.6-43.3ÞkeV nr sets a limit on the elastic, spin-independent WIMP-nucleon scattering cross section for WIMP masses above 8 GeV=c 2 , with a

We perform a low-mass dark matter search using an exposure of 30\,kg$\times$yr with the XENON100 detector. By dropping the requirement of a scintillation signal and using only the ionization signal to determine the interaction energy, we lowered the energy threshold for detection to 0.7\,keV for nuclear recoils. No dark matter detection can be claimed because a complete background model cannot be constructed without a primary scintillation signal. Instead, we compute an upper limit on the WIMP-nucleon scattering cross section under the assumption that every event passing our selection criteria could be a signal event. Using an energy interval from 0.7\,keV to 9.1\,keV, we derive a limit on the spin-independent WIMP-nucleon cross section that excludes WIMPs with a mass of 6\,GeV/$c^2$ above $1.4 \times 10^{-41}$\,cm$^2$ at 90\% confidence level.

According to the majority of regional climate projections, Sub-Saharan Africa (SSA) will likely become warmer in the next decades and rainfall patterns will substantially shift. Understanding the effect of climate variability on food security and poverty and identifying effective adaptation measures in the context of subsistence agriculture is imperative to ensure food security now and in the future. This article presents a micro-level simulation study that was undertaken for Northern Ghana, building on the approach and data developed within a research project of the CGIAR Challenge Programme on Water and Food. The study applied agent-based modelling to analyse how adaptation affects the distribution of household food security and poverty under current climate and price variability. Specifically, we examined the effectiveness of policy interventions related to the promotion of agricultural credit and off-farm employment opportunities. Our simulation experiments suggest that both climate and price variability have a pronounced negative effect on household welfare. Moreover, we found substantial difference in the poverty and food security status of households due to climate and price variability. Provision of agricultural credit and access to off-farm employment are found to be highly effective policy entry points that deserve more empirical research.

Two-neutrino double electron capture (2νECEC) is a second-order weak-interaction process with a predicted half-life that surpasses the age of the Universe by many orders of magnitude1. Until now, indications of 2νECEC decays have only been seen for two isotopes2–5, 78Kr and 130Ba, and instruments with very low background levels are needed to detect them directly with high statistical significance6,7. The 2νECEC half-life is an important observable for nuclear structure models8–14 and its measurement represents a meaningful step in the search for neutrinoless double electron capture—the detection of which would establish the Majorana nature of the neutrino and would give access to the absolute neutrino mass15–17. Here we report the direct observation of 2νECEC in 124Xe with the XENON1T dark-matter detector. The significance of the signal is 4.4 standard deviations and the corresponding half-life of 1.8 × 1022 years (statistical uncertainty, 0.5 × 1022 years; systematic uncertainty, 0.1 × 1022 years) is the longest measured directly so far. This study demonstrates that the low background and large target mass of xenon-based dark-matter detectors make them well suited for measuring rare processes and highlights the broad physics reach of larger next-generation experiments18–20. Two-neutrino double electron capture is observed experimentally in 124Xe with the XENON1T detector, yielding a half-life of 1.8 × 1022 years.

Abstract Climate change will most likely confront agricultural producers with natural, economic, and political conditions that have not previously been observed and are largely uncertain. As a consequence, extrapolation from past data reaches its limits, and a process‐based analysis of farmer adaptation is required. Simulation of changes in crop yields using crop growth models is a first step in that direction. However, changes in crop yields are only one pathway through which climate change affects agricultural production. A meaningful process‐based analysis of farmer adaptation requires a whole‐farm analysis at the farm level. We use a highly disaggregated mathematical programming model to analyze farm‐level climate change adaptation for a mountainous area in southwest Germany. Regional‐level results are obtained by simulating each full‐time farm holding in the study area. We address parameter uncertainty and model underdetermination using a cautious calibration approach and a comprehensive uncertainty analysis. We deal with the resulting computational burden using efficient experimental designs and high‐performance computing. We show that in our study area, shifted crop management time slots can have potentially significant effects on agricultural supply, incomes, and various policy objectives promoted under German and European environmental policy schemes. The simulated effects are robust against model uncertainty and underline the importance of a comprehensive assessment of climate change impacts beyond merely looking at crop yield changes. Our simulations demonstrate how farm‐level models can contribute to a process‐based analysis of climate change adaptation if they are embedded into a systematic framework for treating inherent model uncertainty.

This paper provides an ex-ante assessment of the impacts of climate and price variability on household income and food security in Ethiopia and Ghana. The study applies an agent-based modelling approach to highlight the role of coping and adaptation strategies under climate and price variability. Our simulation results show that climate and price variability adversely affects income and food security of households in both countries. Self-coping mechanisms are found to be important but insufficient to mitigate the adverse effects of variability, implying the need for policy interventions. Adaptation strategies composed of a portfolio of actions such as the provision of production credit and access to improved seeds are found to be effective in reducing the impacts of climate and price variability in Ethiopia. Similarly, policy interventions aimed at improving the provision of short-term production credit along with the existing irrigation facilities are important in Ghana. Finally, this study highlights the importance of capturing the distributional aspects of adaptation options by highlighting heterogeneous effects of variability and adaptation options.

Integrated crop-livestock systems (iCL) are advocated as a promising strategy to increase agricultural production and rehabilitate degraded pastures while mitigating GHG emissions. Although iCL in Brazil has increased over the past few years, it still occupies a small share of the country’s total agricultural area. We investigate the determinants of iCL occurrence in Mato Grosso state, a globally important producer of beef cattle and grains that has experienced rapid land cover change and environmental degradation in recent decades. Our analysis encompasses two typical cases of iCL in Mato Grosso (the rotation of soy followed by pasture, and soy followed by maize and pasture) as well as biophysical, socioeconomic, and institutional factors observable at the household and/or municipality levels that may influence the wide-scale occurrence of iCL. Evidence at both scales suggests that knowledge and supply chain infrastructure play an important role in early occurrence of iCL, as they are more common in regions closer to iCL research stations and processing facilities of grains and cattle. On average iCL adopters are more educated and have better access to technical assistance and sector information than specialized farmers or ranchers. Most iCLs are concentrated near established soy areas and greater similarity exists between municipalities with iCL and soy-dominant municipalities vs. pasture-dominant municipalities. Our findings reveal the importance of specific conditions for iCL occurrence and iCL promotion in livestock-dominant regions. Incentives targeted at ranchers are crucial for the achievement of the Brazilian Government’s goal to restore degraded pastures through agricultural intensification.

Policy makers in developing countries need better evidence of how changes in pesticide regulation would affect pesticide reduction and farm incomes, but there are very few modeling tools that can provide such information. The present study develops a new model based on Mathematical Programming-based Multi-Agent System (MPMAS), a simulation software that allows assessing ex-ante the impact of alternative pesticide use reduction strategies, including combinations of pesticide taxes, the introduction of integrated pest management, a price premium for safe agricultural produce, and subsidies for biopesticides. The model is parameterized with farm and plot level data from northern of Thailand, where the adoption of high-value cash crops has been accompanied by a rapid increase in synthetic pesticide use. Simulation results suggest that a pesticide tax alone has little effect on synthetic pesticide use. A smart policy package – combining integrated pest management, a progressive pesticide tax based on toxicity and subsidies lowering the price of biopesticides – can reduce average use of hazardous pesticides by 34% over current levels without adverse effects on the average farm income.

The XENON1T experiment searches for dark matter particles through their scattering off xenon atoms in a 2 tonne liquid xenon target. The detector is a dual-phase time projection chamber, which measures simultaneously the scintillation and ionization signals produced by interactions in target volume, to reconstruct energy and position, as well as the type of the interaction. The background rate in the central volume of XENON1T detector is the lowest achieved so far with a liquid xenon-based direct detection experiment. In this work we describe the response model of the detector, the background and signal models, and the statistical inference procedures used in the dark matter searches with a 1 tonne$\times$year exposure of XENON1T data, that leaded to the best limit to date on WIMP-nucleon spin-independent elastic scatter cross-section for WIMP masses above 6 GeV/c$^2$.

There has so far been no shared understanding of validity in agent-based simulation. We here conceptualise validation as systematically substantiating the premises on which conclusions from simulation analysis for a particular modelling context are built. Given such a systematic perspective, validity of agent-based models cannot be ensured if validation is merely understood as an isolated step in the modelling process. Rather, valid conclusions from simulation analysis require context-adequate method choices at all steps of the simulation analysis including model construction, model and parameter inference, uncertainty analysis and simulation. We present a twelve-step protocol to highlight the (often hidden) premises for methodological choices and their link to the modelling context. It is designed to aid modelers in understanding their context and in choosing and documenting context-adequate and mutually consistent methods throughout the modelling process. Its purpose is to assist reviewers and the community as a whole in assessing and discussing context-adequacy.

Recent research on land use and land cover change (LUCC) has put more emphasis on the importance of understanding the decision-making of human actors, especially in developing countries. The quest is now for a new generation of LUCC models with a decision-making component. This paper deals with the question of how to realistically represent decision-making in land use models. Two main agent decision architectures are compared. Heuristic agents take sequential decisions following a pre-defined decision tree, while optimizing agents take simultaneous decisions by solving a mathematical programming model. Optimizing behaviour is often discarded as being unrealistic. Yet the paper shows that optimizing agents do have important advantages for empirical land use modelling and that multi-agent systems (MAS) offer an ideal framework for using the strengths of both agent decision architectures. The use of optimization models is advanced with a novel three-stage decision model of investment, production, and consumption to represent uncertainty in models of land use decision-making.

An important goal of modeling human–environment interactions is to provide scientific information to policymakers and stakeholders in order to better support their planning and decision-making processes. Modern technologies in the fields of GIS and data processing, together with an increasing amount of accessible information, have the potential to meet the varying information needs of policymakers and stakeholders. Multiagent modeling holds the promise of providing an enhanced collaborative framework in which planners, modelers, and stakeholders may learn and interact. The fulfillment of this promise, however, depends on the empirical parameterization of multiagent models. Although multiagent models have been widely applied in experimental and hypothetical settings, only few studies have strong linkages to empirical data and the literature on methods of empirical parameterization is still limited. This paper presents a straightforward approach to parameterize multiagent models in applied development research. The parameterization uses a common sampling frame to randomly select observation units for both biophysical measurements and socioeconomic surveys. The biophysical measurements, i.e., soil properties in this study, are then extrapolated over the landscape using multiple regressions and a digital elevation model. The socioeconomic surveys are used to estimate probability functions for key characteristics of human actors, which are then assigned to the model agents with Monte Carlo techniques. This approach generates a landscape and agent populations that are robust and statistically consistent with empirical observations.

Complex combinations of biophysical and socio-economic constraints characterize the less-favored rural areas in developing countries. More so, these constraints are diverse as they vary considerably between households even in the same community. We propose multi-agent systems as a modeling approach well suited for capturing the complexity of constraints as well as the diversity in which they appear at the farm household level. Given that empirical multi-agent models based on mathematical programming share the characteristics of bio-economic farm models plus some additional features, one may interpret bio-economic farm models as a special case of multi-agent models without spatial dimension and direct interaction. Evidently, spatially explicit, connected multi-agent models have higher requirements in terms of development costs, empirical data and validation. Therefore, we see them as a complement, and not a substitute, to existing bio-economic modeling approaches. They might be the preferred model choice when heterogeneity and interactions of agents and environments are significant and, therefore, policy responses cannot be aggregated linearly. We illustrate the strength of empirical multi-agent models with simulation results from Uganda and Chile and indicate how they may assist policymakers in prioritizing and targeting alternative policy interventions especially in less-favored areas.

Soil loss, nutrient depletion and land degradation contribute to the skimpy performance of smallholder agriculture and pose serious policy challenges in developing countries. Surprisingly, natural resource management practices that enhance sustainability while improving productivity have not been fully adopted despite continuous efforts of promotion. Using data collected from 2901 farm households in the Farmers Innovation Fund (FIF) of the World Bank, this study examines factors delaying adoption of resource management and farming practices from the perspective of social learning and network size. Specifically, the study aims at identifying the extent to which differences in network structure matter in providing opportunities to learn about new ways of sustainable resource management practices using regression analysis. The result confirms that social network size plays a significant role in enhancing adoption of natural resource management practices. Moreover, external sources of information such as extension provision play a crucial role in enhancing adoption of resource management practices. Thus, future endeavours should link extension services to informal networks to enhance adoption of sustainable natural resource management practices.

This paper examines the livelihood outcomes and adaptation strategies of households who have been involuntarily resettled from the project area of the Son La Hydropower Project in Vietnam to a remote mountain location with an intense scarcity of resources. We collected household data using a double recall, referring to the situation before and after resettlement, and for both the resettled and host households. The results show that resettled households lost income mainly because of a loss in crop output. In response, they tried to intensify crop production by using more fertilizers. The distribution of their farm output and income became less equal after resettlement although land had been distributed equally to all households. The host households had a greater number of opportunities to adapt and increased the cropping frequency of rice, intensified mineral fertilizer use and intensified livestock production, and as a result, their farm output and incomes increased. The livelihood adaptation of both the host and resettled households was strongly conditioned by a lack of available livelihood assets in this remote mountain location; it is therefore questionable whether households will be able to maintain their livelihood outcomes in the long run.

The rapid growth in pesticide use is a significant problem for Thailand, as it is in many other developing countries with an intensifying agriculture. The objective of this study was to quantify how much of the total quantity of pesticides is overused. The novelty of this research resides in the fact that it considered the social rather than the private optimum by including negative pesticide externalities in determining levels of overuse. Marginal benefits of pesticides are quantified by estimating Cobb–Douglas production functions with an exponential damage control specification. The marginal costs are calculated as the sum of private and external costs with the latter quantified using the Pesticide Environmental Accounting (PEA) tool. The method is applied using farm- and plot-level data from one intensive upland vegetable production system in northern Thailand. The findings show that about 80% of the applied pesticide quantity is used in excess of the social optimum, while the difference between the private and social level of overuse is small for this particular case study. Therefore results from the study area suggest that internalizing pesticide externalities into the price of pesticides would only have a small effect on reducing pesticide overuse.

The radiation environment at the Martian surface is, apart from occasional solar energetic particle events, dominated by galactic cosmic radiation, secondary particles produced in their interaction with the Martian atmosphere and albedo particles from the Martian regolith. The highly energetic primary cosmic radiation consists mainly of fully ionized nuclei creating a complex radiation field at the Martian surface. This complex field, its formation and its potential health risk posed to astronauts on future manned missions to Mars can only be fully understood using a combination of measurements and model calculations. In this work the outcome of a workshop held in June 2016 in Boulder, CO, USA is presented: experimental results from the Radiation Assessment Detector of the Mars Science Laboratory are compared to model results from GEANT4, HETC-HEDS, HZETRN, MCNP6, and PHITS. Charged and neutral particle spectra and dose rates measured between 15 November 2015 and 15 January 2016 and model results calculated for this time period are investigated.

The XENON1T experiment at the Laboratori Nazionali del Gran Sasso is the most sensitive direct detection experiment for dark matter in the form of weakly interacting particles (WIMPs) with masses above $6\text{ }\text{ }\mathrm{GeV}/{c}^{2}$ scattering off nuclei. The detector employs a dual-phase time projection chamber with 2.0 metric tons of liquid xenon in the target. A one $\text{metric}\text{ }\text{ton}\ifmmode\times\else\texttimes\fi{}\mathrm{year}$ exposure of science data was collected between October 2016 and February 2018. This article reports on the performance of the detector during this period and describes details of the data analysis that led to the most stringent exclusion limits on various WIMP-nucleon interaction models to date. In particular, signal reconstruction, event selection, and calibration of the detector response to nuclear and electronic recoils in XENON1T are discussed.

Xenon dual-phase time projection chambers designed to search for Weakly Interacting Massive Particles have so far shown a relative energy resolution which degrades with energy above $\sim$200 keV due to the saturation effects. This has limited their sensitivity in the search for rare events like the neutrinoless double-beta decay of $^{136}$Xe at its $Q$-value, $Q_{\beta\beta}\simeq$ 2.46 MeV. For the XENON1T dual-phase time projection chamber, we demonstrate that the relative energy resolution at 1 $\sigma/\mu$ is as low as (0.80$\pm$0.02) % in its one-ton fiducial mass, and for single-site interactions at $Q_{\beta\beta}$. We also present a new signal correction method to rectify the saturation effects of the signal readout system, resulting in more accurate position reconstruction and indirectly improving the energy resolution. The very good result achieved in XENON1T opens up new windows for the xenon dual-phase dark matter detectors to simultaneously search for other rare events.

Declining soil fertility and increasing rural poverty are major problems facing sub-Saharan agriculture. Bio-economic modeling has been used to analyze the complex interaction between ecological sustainability and rural poverty as well as to explore policy options promoting sustainable development. This paper shows that these models can be further advanced by adopting an agent-based modeling approach. This gives a more realistic representation of diversity in socioeconomic and biophysical terms, allows local interaction between households, and can yield an ex-ante assessment of the distributional consequences of policy intervention. This paper describes the modeling approach and illustrates it with an empirical application to two village communities in the Lake Victoria Crescent of Uganda. It is shown how the model system can be calibrated with and validated against empirical data. The model is used to analyze the potential effect of short-term credit, mineral fertilizer, and improved maize seed on poverty and sustainability. Simulation results suggest substantial reductions in poverty although the incidence of poverty would remain high and these innovations alone would have little effect on the long-term ecological sustainability of the system.

Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomieVolume 57, Issue 4 p. 417-429 An Overview of Computational Modeling in Agricultural and Resource Economics James Nolan, James Nolan Department of Bioresource Policy, Business and Economics, University of Saskatchewan, Saskatoon, Canada, SK S7N 5A8 (corresponding author: phone: 306-966-8412; fax: 306-966-8413; e-mail: james.nolan@usask.ca).Search for more papers by this authorDawn Parker, Dawn Parker School of Planning, Faculty of Environment, University of Waterloo, EV1 306, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1 (phone: 1-519-888-4567, ext. 38888; fax: 1-519 725-2827; e-mail: dcparker@connect.uwaterloo.ca).Search for more papers by this authorG. Cornelis Van Kooten, G. Cornelis Van Kooten Department of Economics, University of Victoria, PO Box 1700, Stn CSC, Victoria, British Columbia, Canada, BC V8W 2Y2 (phone: 250-721-8539; fax: 250-721-6214; e-mail: vkooten@uvic.ca).Search for more papers by this authorThomas Berger, Thomas Berger Department of Land Use Economics in the Tropics and Subtropics, Universität Hohenheim (490d), 70593 Stuttgart, Germany (phone: (49) 711-459-24116; fax: (49) 711-459-24248; e-mail: i490d@uni-hohenheim.de).Search for more papers by this author James Nolan, James Nolan Department of Bioresource Policy, Business and Economics, University of Saskatchewan, Saskatoon, Canada, SK S7N 5A8 (corresponding author: phone: 306-966-8412; fax: 306-966-8413; e-mail: james.nolan@usask.ca).Search for more papers by this authorDawn Parker, Dawn Parker School of Planning, Faculty of Environment, University of Waterloo, EV1 306, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1 (phone: 1-519-888-4567, ext. 38888; fax: 1-519 725-2827; e-mail: dcparker@connect.uwaterloo.ca).Search for more papers by this authorG. Cornelis Van Kooten, G. Cornelis Van Kooten Department of Economics, University of Victoria, PO Box 1700, Stn CSC, Victoria, British Columbia, Canada, BC V8W 2Y2 (phone: 250-721-8539; fax: 250-721-6214; e-mail: vkooten@uvic.ca).Search for more papers by this authorThomas Berger, Thomas Berger Department of Land Use Economics in the Tropics and Subtropics, Universität Hohenheim (490d), 70593 Stuttgart, Germany (phone: (49) 711-459-24116; fax: (49) 711-459-24248; e-mail: i490d@uni-hohenheim.de).Search for more papers by this author First published: 15 October 2009 https://doi.org/10.1111/j.1744-7976.2009.01163.xCitations: 49Read the full textAboutRelatedInformationPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessClose modalShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume57, Issue4December 2009Pages 417-429 RelatedInformation RecommendedAgricultural economics and distributional effectsJoachim Von Braun, Agricultural EconomicsAntipodean agricultural and resource economics at 60: farm managementBill Malcolm, Vic Wright, Australian Journal of Agricultural and Resource EconomicsBridging behavioural factors and standard bio‐economic modelling in an agent‐based modelling frameworkRobert Huber, Hang Xiong, Kevin Keller, Robert Finger, Journal of Agricultural EconomicsOn the economics of agricultural productionJean-Paul Chavas, Australian Journal of Agricultural and Resource Economics

Abstract. We present a comprehensive, interdisciplinary project which demonstrates that large-scale plantations of Jatropha curcas – if established in hot, dry coastal areas around the world – could capture 17–25 t of carbon dioxide per hectare per year from the atmosphere (over a 20 yr period). Based on recent farming results it is confirmed that the Jatropha curcas plant is well adapted to harsh environments and is capable of growing alone or in combination with other tree and shrub species with minimal irrigation in hot deserts where rain occurs only sporadically. Our investigations indicate that there is sufficient unused and marginal land for the widespread cultivation of Jatropha curcas to have a significant impact on atmospheric CO2 levels at least for several decades. In a system in which desalinated seawater is used for irrigation and for delivery of mineral nutrients, the sequestration costs were estimated to range from 42–63 EUR per tonne CO2. This result makes carbon farming a technology that is competitive with carbon capture and storage (CCS). In addition, high-resolution simulations using an advanced land-surface–atmosphere model indicate that a 10 000 km2 plantation could produce a reduction in mean surface temperature and an onset or increase in rain and dew fall at a regional level. In such areas, plant growth and CO2 storage could continue until permanent woodland or forest had been established. In other areas, salinization of the soil may limit plant growth to 2–3 decades whereupon irrigation could be ceased and the captured carbon stored as woody biomass.

This paper presents a micro-level simulation study on possible impacts of farm level adaptation strategies using a spatial dynamic hydro-economic model called Mathematical Programming based Multi Agent System. The model was validated for the Northern semi-arid region of Ghana. The simulation results revealed that climate variability has substantial impacts on the poverty and food security status of farm households. Policy interventions like the provision of agricultural credit and expansion of irrigation access are found to be highly important in reducing the adverse effects of climate variability for the capital constrained and poor rainfed farm households. However, to achieve significant changes in food security, a mix of adaptation strategies in the form of credit and irrigation has to be provided simultaneously. We also found that farm level adaption through shifting planting date as well as adopting early maturing crop varieties can substantially reduce the adverse impacts of climate variability.

We present the first constraints on the spin-dependent, inelastic scattering cross section of Weakly Interacting Massive Particles (WIMPs) on nucleons from XENON100 data with an exposure of 7.64$\times$10$^3$\,kg\,day. XENON100 is a dual-phase xenon time projection chamber with 62\,kg of active mass, operated at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy and designed to search for nuclear recoils from WIMP-nucleus interactions. Here we explore inelastic scattering, where a transition to a low-lying excited nuclear state of $^{129}$Xe is induced. The experimental signature is a nuclear recoil observed together with the prompt de-excitation photon. We see no evidence for such inelastic WIMP-$^{129}$Xe interactions. A profile likelihood analysis allows us to set a 90\% C.L. upper limit on the inelastic, spin-dependent WIMP-nucleon cross section of $3.3 \times 10^{-38}$\,cm$^{2}$ at 100\,GeV/c$^2$. This is the most constraining result to date, and sets the pathway for an analysis of this interaction channel in upcoming, larger dual-phase xenon detectors.

We report on a search for electronic recoil event rate modulation signatures in the XENON100 data accumulated over a period of 4 years, from January 2010 to January 2014. A profile likelihood method, which incorporates the stability of the XENON100 detector and the known electronic recoil background model, is used to quantify the significance of periodicity in the time distribution of events. There is a weak modulation signature at a period of $431^{+16}_{-14}$ days in the low energy region of $(2.0-5.8)$ keV in the single scatter event sample, with a global significance of $1.9\,\sigma$, however no other more significant modulation is observed. The expected annual modulation of a dark matter signal is not compatible with this result. Single scatter events in the low energy region are thus used to exclude the DAMA/LIBRA annual modulation as being due to dark matter electron interactions via axial vector coupling at $5.7\,\sigma$.

Agriculture in many industrialized countries is subject to a wide range of policy interventions that seek to achieve ambitious climate, energy and environment-related objectives. Increasing support for the generation of climate-friendly, renewable energy in agriculture, however, may lead to potential conflicts with agri-environmental policies aimed at land use extensification and landscape preservation. These potential trade-offs and inconsistencies in terms of policy implementation are not yet well understood, since conventional tools for agricultural economic assessment work on an aggregate regional level and do not fully capture the likely farmer responses when making a choice between investments in biogas production and participation in agri-environmental policy schemes. We employed a farm-level model to analyze the reaction of a heterogeneous farming population in Southwest Germany to the incentives set by the German Renewable Energy Act (EEG), on the one hand, and the agri-environmental policy scheme MEKA, on the other. Our simulations indicate a potentially large decrease of MEKA participation due to biogas production supported under EEG. The success of the 2012 EEG revision in reducing the ‘maizification’ of agricultural landscapes will critically depend on the local demand for biogas excess heat. In any case, the EEG revision does not alleviate conflicts between the expansion of renewable energy and environmental considerations, but rather shifts priorities from the former to the later: the simulated reductions of maize areas are achieved by a considerable reduction in overall biogas production (“output effect”), and not by encouraging less maize-intensive feedstock mixes (“substitution effect”).

Rural producer organizations (RPOs) are currently seen as mechanisms of reducing transaction costs and improving market access of smallholder farmers. Yet little is known about the determinants of RPO effectiveness, especially in Sub-Saharan African countries. In this article we assess functioning of Ugandan RPO using a combination of participatory research and survey methods. We recommend areas for development interventions that would enhance the positive impact of RPO on livelihoods of their members. The proposed interventions refer to monetary transactions between RPO and their members, information channels within RPO, access to inputs and finance, member knowledge capacity and motivation of leaders.

We report on WIMP search results in the XENON100 detector using a non-relativistic effective field theory approach. The data from science run II (34 kg $\times$ 224.6 live days) was re-analyzed, with an increased recoil energy interval compared to previous analyses, ranging from $(6.6 - 240)~\mathrm{keV_\mathrm{nr}}$. The data is found to be compatible with the background-only hypothesis. We present 90% confidence level exclusion limits on the coupling constants of WIMP-nucleon effective operators using a binned profile likelihood method. We also consider the case of inelastic WIMP scattering, where incident WIMPs may up-scatter to a higher mass state, and set exclusion limits on this model as well.

Abstract Climate variability with unexpected droughts and floods causes serious production losses and worsens food security, especially in Sub‐Saharan Africa. This study applies stochastic bioeconomic modeling to analyze smallholder adaptation to climate and price variability in Ethiopia. It uses the agent‐based simulation package Mathematical Programming‐based Multi‐Agent Systems (MPMAS) to capture nonseparable production and consumption decisions at household level, considering livestock and eucalyptus sales for consumption smoothing, as well as farmer responses to policy interventions. We find the promotion of new maize and wheat varieties to be an effective adaptation option, on average, especially when accompanied by policy interventions such as credit and fertilizer subsidy. We also find that the effectiveness of available adaptation options is quite different across the heterogeneous smallholder population in Ethiopia. This implies that policy assessments based on average farm households may mislead policy makers to adhere to interventions that are beneficial on average albeit ineffective in addressing the particular needs of poor and food insecure farmers.

The rate and location of cropland expansion onto cattle pastures in Brazil could affect global food security, climate change, and economic growth. We combined mapping, statistical modeling, and qualitative methods to investigate patterns and processes of pasture to crop conversion (P2C) in Mato Grosso State (MT), Brazil, a globally important center of agricultural production. P2C land constituted 49% of cropland expansion from 2000 to 2013. For a random sample of ̃250 m pixels in MT, we estimated a regression model skilled at predicting P2C land in the rest of the state as a function of cattle ranching suitability, cropping suitability, and P2C conversion costs. Surprisingly, just 1/7 of pasture agronomically suitable for cultivation had undergone P2C. Hedonic regressions revealed that agronomic characteristics of land were associated with less than 20% of the variation in cropland suitability. Instead, the majority of the variation stemmed from a combination of proximity to agricultural infrastructure, characteristics of neighboring lands, and time fixed effects. The weak relationship between agronomic characteristics of land and P2C location suggests a less certain future for P2C than projections made with agronomic models. Consequentially, complications may arise for greenhouse gas mitigation policies in Brazil predicated on widespread expansion of cropland on pasture vs. natural areas. Our follow-up qualitative research shows that because P2C has often involved land rentals or sales, poorly functioning land institutions may have constrained P2C. Locally poor land quality, omitted from agronomic P2C predictions, can either catalyze or constrain P2C by limiting returns to ranching, farming, or both. Interventions to control rates and locations of P2C should take these insights into account.

The XENON1T experiment aims for the direct detection of dark matter in a detector filled with 3.3 tons of liquid xenon. In order to achieve the desired sensitivity, the background induced by radioactive decays inside the detector has to be sufficiently low. One major contributor is the $$\beta $$ -emitter $$^{85}$$ Kr which is present in the xenon. For XENON1T a concentration of natural krypton in xenon $$\mathrm {^{nat}\mathrm{Kr/Xe}\,<\,200\,ppq}$$ (parts per quadrillion, $$1~\mathrm{ppq}~=10^{-15} \mathrm{mol/mol}$$ ) is required. In this work, the design, construction and test of a novel cryogenic distillation column using the common McCabe–Thiele approach is described. The system demonstrated a krypton reduction factor of $$6.4\cdot 10^5$$ with thermodynamic stability at process speeds above 3 kg/h. The resulting concentration of $$\mathrm {^{nat}\mathrm{Kr/Xe}<26\,ppq}$$ is the lowest ever achieved, almost one order of magnitude below the requirements for XENON1T and even sufficient for future dark matter experiments using liquid xenon, such as XENONnT and DARWIN.

The XENON1T dark matter experiment aims to detect weakly interacting massive particles (WIMPs) through low-energy interactions with xenon atoms. To detect such a rare event necessitates the use of radiopure materials to minimize the number of background events within the expected WIMP signal region. In this paper we report the results of an extensive material radioassay campaign for the XENON1T experiment. Using gamma-ray spectroscopy and mass spectrometry techniques, systematic measurements of trace radioactive impurities in over one hundred samples within a wide range of materials were performed. The measured activities allowed for stringent selection and placement of materials during the detector construction phase and provided the input for XENON1T detection sensitivity estimates through Monte Carlo simulations.

Whether terrestrial life can withstand the martian environment is of paramount interest for planetary protection measures and space exploration. To understand microbial survival potential in Mars-like conditions, several fungal and bacterial samples were launched in September 2019 on a large NASA scientific balloon flight to the middle stratosphere (∼38 km altitude) where radiation levels resembled values at the equatorial Mars surface. Fungal spores of Aspergillus niger and bacterial cells of Salinisphaera shabanensis , Staphylococcus capitis subsp. capitis , and Buttiauxella sp. MASE-IM-9 were launched inside the MARSBOx (Microbes in Atmosphere for Radiation, Survival, and Biological Outcomes Experiment) payload filled with an artificial martian atmosphere and pressure throughout the mission profile. The dried microorganisms were either exposed to full UV-VIS radiation (UV dose = 1148 kJ m −2 ) or were shielded from radiation. After the 5-h stratospheric exposure, samples were assayed for survival and metabolic changes. Spores from the fungus A. niger and cells from the Gram-(–) bacterium S. shabanensis were the most resistant with a 2- and 4-log reduction, respectively. Exposed Buttiauxella sp. MASE-IM-9 was completely inactivated (both with and without UV exposure) and S. capitis subsp. capitis only survived the UV shielded experimental condition (3-log reduction). Our results underscore a wide variation in survival phenotypes of spacecraft associated microorganisms and support the hypothesis that pigmented fungi may be resistant to the martian surface if inadvertently delivered by spacecraft missions.

Until 2019, the Brazilian federal government employed a number of policy measures to fulfill the pledge of reducing greenhouse gas emissions from land use change and agriculture. While its forest law enforcement strategy was partially successful in combating illegal deforestation, the effectiveness of positive incentive measures in agriculture has been less clear. The reason is that emissions reduction from market-based incentives such as the Brazilian Low-Carbon Agriculture Plan cannot be easily verified with current remote sensing monitoring approaches. Farmers have adopted a large variety of integrated land-use systems of crop, livestock and forestry with highly diverse per-hectare carbon balances. Their responses to policy incentives were largely driven by cost and benefit considerations at the farm level and not necessarily aligned with federal environmental objectives. This article analyzes climate-related land-use policies in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize cropping systems prevail. We employ agent-based bioeconomic simulation together with life-cycle assessment to explicitly capture the heterogeneity of farm-level costs, benefits of adoption, and greenhouse gas emissions. Our analysis confirms previous assessments but suggests a smaller farmer policy response when measured as increase in area of integrated systems. In terms of net carbon balances, our simulation results indicate that mitigation effects at the farm level depended heavily on the exact type of livestock and grazing system. The available data were insufficient to rule out even adverse effects. The Brazilian experience thus offers lessons for other land-rich countries that build their climate mitigation policies on economic incentives in agriculture.

Abstract This study uses an agent‐based model for ex ante assessment of agricultural innovations. The model builds on whole farm mathematical programming (MP) and extends the methodology with a spatial representation of the system, the heterogeneity of farm households and landscapes, and the interaction between farm households. We apply the model in a northern Thai watershed to study the potential of four innovations to increase the profitability of litchi orchards. Cost‐benefit analysis shows that each innovation would increase the profitability of litchi growing; however, the results of the agent‐based model show that at current price levels these innovations alone would not be enough to stem the decline in the area under litchis. The model was validated and the sensitivity of the results tested for variations in the irrigated water supply and liquidity. We report on how farmers responded to these results and discuss the implications for other areas in northern Thailand.

This paper studies the diffusion of greenhouse agriculture in a watershed in the northern uplands of Thailand by applying econometrics and agent‐based modeling in combination. Adoption has been rapid by farmers in the central valley of the watershed, while farmers at higher altitudes, lacking transferable land titles that could serve as mortgage collateral, have been unable to obtain loans for greenhouse investment. The objectives of the paper are both methodological and empirical. On the methodological side, it shows that econometrically estimated models of farm household behavior are useful to design and to parameterize an agent‐based model. On the empirical side, simulation results show that if mortgage collateral would not be required, then adoption in the upper part of the watershed could reach nearly 77% of farm households by 2020, as compared to about 36% under current conditions. Furthermore results suggest a significant increase in incomes related to the innovation and a substantially greater irrigation water use, especially in the central part. As bell pepper under greenhouses has replaced pesticide‐intensive chrysanthemum, it has declined average levels of pesticide use. Nevertheless, pesticide use is high and farmers are struggling to control pests, which raises questions about the long‐term sustainability of the innovation. Dans le présent article, nous avons analysé, à l'aide d'un modèle économétrique et d'un modèle multi‐agent, l'expansion de la culture en serre dans un bassin versant des hautes terres du Nord de la Thaïlande. Les agriculteurs de la vallée centrale du bassin versant ont adopté rapidement cette forme d'agriculture, tandis que les agriculteurs installés dans les hautes altitudes n’ont pu, faute de titres fonciers transférables pouvant servir de garantie, obtenir de prêts pour construire des serres. Les objectifs du présent article étaient à la fois méthodologiques et empiriques. Sur le plan méthodologique, notre étude a montré que les modèles de comportement des ménages agricoles estimés économétriquement sont utiles pour concevoir et paramétrer un modèle multi‐agent. Sur le plan empirique, les résultats de simulation ont montré que, si des garanties de prêt n’étaient pas exigées, 77 p. 100 des ménages agricoles adopteraient la culture en serre dans les hautes terres du bassin versant d'ici 2020, comparativement à environ 36 p. 100 dans les conditions actuelles. De nouveaux résultats ont indiqué que cette innovation ainsi qu’un usage accru de l'eau pour l'irrigation, particulièrement dans la partie centrale, pourraient générer une hausse substantielle des revenus. Depuis que la culture en serre du poivron vert a remplacé la culture du chrysanthème exigeante en pesticides, l'usage des pesticides a beaucoup diminué, mais demeure tout de même élevé. Les agriculteurs ont de la difficultéà lutter contre les ravageurs, ce qui soulève des questions sur la viabilitéà long terme de l'innovation.

Soil degradation is an environmental process mainly caused by land use decision-makers that has substantial feedback effects on livelihoods and the environment. To capture these feedback effects and the resulting human-environment interactions, we used an agent-based modeling approach to couple two software packages that simulate soil, water and plant dynamics (LUCIA), and farm decision-making (MP-MAS). We show that such a software coupling approach has advantages over hard-coded model integration as applied by most other comparable studies, as it facilitates combining of increasingly sophisticated individual models and can achieve a well-balanced representation of agricultural systems. Using a numerical application for a small mountainous watershed in northwest Vietnam we show the challenges in model coupling, calibration and partial validation, and explore the properties of the coupled model system. Scenario analysis covering the introduction of low-cost soil conservation techniques indicates that some of these techniques would have an impact on soil erosion, maize productivity and household income levels in the study catchment area under current conditions. However, maize yields and the adoption of soil conservation appear to be sensitive to the price of mineral fertilizers, with lower fertilizer prices impeding the adoption of soil conservation measures. The software coupling approach was able to capture interactions between decision-makers and natural resources, as well as the level of spatial variability, in more detail than the individual models. Still, the greater number of endogenous variables and thus degrees of freedom increased the importance of validation and testing parameter sensitivity of the results.

We describe the purification of xenon from traces of the radioactive noble gas radon using a cryogenic distillation column. The distillation column was integrated into the gas purification loop of the XENON100 detector for online radon removal. This enabled us to significantly reduce the constant $$^{222}$$ Rn background originating from radon emanation. After inserting an auxiliary $$^{222}$$ Rn emanation source in the gas loop, we determined a radon reduction factor of $$R\,>\,27$$ (95% C.L.) for the distillation column by monitoring the $$^{222}$$ Rn activity concentration inside the XENON100 detector.

Received 24 February 2017DOI:https://doi.org/10.1103/PhysRevD.95.059901© 2017 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasDark matterParticle dark matterPhysical SystemsWeakly interacting massive particlesTechniquesDark matter detectorsParticles & FieldsGravitation, Cosmology & Astrophysics

We report results from searches for new physics with low-energy electronic recoil data recorded with the XENON1T detector, with an exposure of 0.65 t-y and an unprecedentedly low background rate of $76 \pm 2_{stat}$ events/(t y keV) between 1-30 keV. An excess over known backgrounds is observed below 7 keV, rising towards lower energies and prominent between 2-3 keV. The solar axion model has a 3.5$\sigma$ significance, and a three-dimensional 90% confidence surface is reported for axion couplings to electrons, photons, and nucleons. This surface is inscribed in the cuboid defined by $g_{ae} < 3.7 \times 10^{-12}$, $g_{ae}g_{an}^{eff} < 4.6 \times 10^{-18}$, and $g_{ae}g_{a\gamma} < 7.6\times10^{-22}~{GeV}^{-1}$, and excludes either $g_{ae}=0$ or $g_{ae}g_{a\gamma}=g_{ae}g_{an}^{eff}=0$. The neutrino magnetic moment signal is similarly favored over background at 3.2$\sigma$ and a confidence interval of $\mu_{\nu} \in (1.4,~2.9)\times10^{-11}\mu_B$ (90% C.L.) is reported. Both results are in strong tension with stellar constraints. The excess can also be explained by $\beta$ decays of tritium at 3.2$\sigma$ significance with a corresponding tritium concentration in xenon of $(6.2 \pm 2.0) \times 10^{-25}$ mol/mol. Such a trace amount can be neither confirmed nor excluded with current knowledge of production and reduction mechanisms. The significances of the solar axion and neutrino magnetic moment hypotheses are decreased to 2.1$\sigma$ and 0.9$\sigma$, respectively, if an unconstrained tritium component is included in the fitting. With respect to bosonic dark matter, the excess favors a monoenergetic peak at ($2.3 \pm 0.2$) keV (68% C.L.) with a 3.0$\sigma$ global (4.0$\sigma$ local) significance over background. This analysis sets the most restrictive direct constraints to date on pseudoscalar and vector bosonic dark matter for most masses between 1 and 210 keV/c$^2$.

Abstract The selection of low-radioactive construction materials is of utmost importance for the success of low-energy rare event search experiments. Besides radioactive contaminants in the bulk, the emanation of radioactive radon atoms from material surfaces attains increasing relevance in the effort to further reduce the background of such experiments. In this work, we present the $$^{222}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>222</mml:mn> </mml:msup> </mml:math> Rn emanation measurements performed for the XENON1T dark matter experiment. Together with the bulk impurity screening campaign, the results enabled us to select the radio-purest construction materials, targeting a $$^{222}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>222</mml:mn> </mml:msup> </mml:math> Rn activity concentration of $$10\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>10</mml:mn> <mml:mspace /> <mml:mspace /> <mml:mi>μ</mml:mi> <mml:mi>Bq</mml:mi> <mml:mo>/</mml:mo> <mml:mi>kg</mml:mi> </mml:mrow> </mml:math> in $$3.2\,\mathrm{t}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.2</mml:mn> <mml:mspace /> <mml:mi>t</mml:mi> </mml:mrow> </mml:math> of xenon. The knowledge of the distribution of the $$^{222}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>222</mml:mn> </mml:msup> </mml:math> Rn sources allowed us to selectively eliminate problematic components in the course of the experiment. The predictions from the emanation measurements were compared to data of the $$^{222}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>222</mml:mn> </mml:msup> </mml:math> Rn activity concentration in XENON1T. The final $$^{222}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>222</mml:mn> </mml:msup> </mml:math> Rn activity concentration of $$(4.5\pm 0.1)\,\mathrm{\,}\upmu \mathrm{Bq}/\mathrm{kg}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>4.5</mml:mn> <mml:mo>±</mml:mo> <mml:mn>0.1</mml:mn> <mml:mo>)</mml:mo> <mml:mspace /> <mml:mspace /> <mml:mi>μ</mml:mi> <mml:mi>Bq</mml:mi> <mml:mo>/</mml:mo> <mml:mi>kg</mml:mi> </mml:mrow> </mml:math> in the target of XENON1T is the lowest ever achieved in a xenon dark matter experiment.

Whole-farm mathematical programming (MP) models help to understand the complex implications of farm planning decisions taking the full agricultural system into account. Applying optimization models in direct interaction with farmers goes beyond leading to improved recommendations for mixed crop-livestock farm systems. The tacit farmer knowledge revealed in such interaction sessions can also provide valuable input for descriptive and predictive models of farmer behavior in scientific analyses of agricultural policy or technology adoption. To date, a conceptual and technical knowledge divide has prevented the widespread use of MP in interactive modeling with farmers in the field. This article revisits the rationale for interactive MP modeling and explores whether it can be upheld in practice. We evaluate whether a lightweight graphical interface that can be quickly adapted to practical applications of whole-farm planning can help bridge the gap between models and users. We present a user interface for whole-farm MP models implemented in the open-source statistical programming language R. We apply the tool in a participatory research process in Paraguay, analyzing the opportunities and barriers to adoption of new agroforestry options for smallholder farmers. Using in-depth interviews and participant feedback, we evaluate the tool's accessibility, credibility and relevance as well as its contribution to bridging knowledge gaps in the overall research process. In our explorative case study, interactive MP modeling provided essential insights into the local farming system and was highly accepted by smallholder farmers and extension workers. We observed a high interest in structured farm analysis, planning and education for smallholder agriculture. The lightweight user interface contributed to efficient and successful interaction between models and users. Interactive modeling helps to improve the application of whole-farm MP modeling in agricultural systems analysis. Case study examples and a quickly customizable interface can lower the barriers for using interactive MP modeling with farmers and other stakeholders. With the recent wave of digitalization in agriculture, the insights obtained here are also relevant for the development of digital farm management tools for systematic whole-farm planning.

Agriculture in mountainous areas in Vietnam has much intensified since the introduction of market-based reforms in the mid 1980s. The adoption of hybrid maize varieties, mineral fertilizers and reduction in fallow periods has improved farm incomes, but has also led to a dramatic increase in soil erosion from sloping lands which has created a downward pressure on crop yields and has had adverse effects on downstream areas. This study explores the relationship between soil fertility, crop yields and the use of soil conservation methods by applying an agent-based modeling approach that combines whole-farm mathematical programming to simulate the decision-making of each individual farm household with a biophysical simulator of crop yields and soil fertility dynamics for each individual landscape unit. Simulation results suggest an average soil loss is 30 tons for maize fields and 27 tons for cassava fields per hectare per annum under present economic conditions, which is in the range of what other studies have measured, and a consequent decline in the average household incomes by 28.5% over a 25 years period. The introduction of three soil conservation methods in maize (vetiver grass strips, ruzi grass barriers and leucaena hedges) shows that these are not economical for farm households to adopt under present conditions, chiefly because of lower short-term maize yields. We explore the effect of giving farm households monetary incentives to adopt soil conservation and find that the payment needed for reducing 40 ± 2% of the estimated soil loss would be about 12–16 USD per ton of soil saved.

Irrigation with surface water enables Chilean agricultural producers to generate one of the country's most important economic exports. The Chilean water code established tradable water rights as a mechanism to allocate water amongst farmers and other water-use sectors. It remains contested whether this mechanism is effective and many authors have raised equity concerns regarding its impact on water users. For example, speculative hoarding of water rights in expectations of their increasing value has been described. This paper demonstrates how farmers can hoard water rights as a risk management strategy for variable water supply, for example, due to the cycles of El Niño or as consequence of climate change. While farmers with insufficient water rights can rely on unclaimed water during conditions of normal water availability, drought years overproportionally impact on their supply of irrigation water and thereby farm profitability. This study uses a simulation model that consists of a hydrological balance model component and a multiagent farm decision and production component. Both model components are parameterized with empirical data, while uncertain parameters are calibrated. The study demonstrates a thorough quantification of parameter uncertainty, using global sensitivity analysis and multiple behavioral parameter scenarios.

The need to balance agricultural production and environmental protection shifted the focus of Brazilian land-use policy toward sustainable agriculture. In 2010, Brazil established preferential credit lines to finance investments into low-carbon integrated agricultural systems of crop, livestock and forestry. This article presents a simulation-based empirical assessment of integrated system adoption in the state of Mato Grosso, where highly mechanized soybean–cotton and soybean–maize double-crop systems currently prevail. We employ bioeconomic modeling to explicitly capture the heterogeneity of farm-level costs and benefits of adoption. By parameterizing and validating our simulations with both empirical and experimental data, we evaluate the effectiveness of the ABC Integration credit through indicators such as land-use change, adoption rates and budgetary costs of credit provision. Alternative scenarios reveal that specific credit conditions might speed up the diffusion of low-carbon agricultural systems in Mato Grosso.

Agricultural development in lower-income countries and resulting increases in agricultural productivity are generally accompanied by a shift from extensive to more intensive types of land use. The objective of this paper is to analyze how pest and plant disease management among smallholder farmers has changed along with the process of land use intensification, the aim being to identify constraints as well as possible approaches to the use of more sustainable pest and plant disease control practices. Using survey data from 240 smallholder farms located in the upland areas of northern Thailand, we show that land use intensification is accompanied by a reduction in the use of traditional methods of pest management and an increase in the use of synthetic pesticides. While farms with a low level of land use intensity sprayed on average twice a year and used a total of 1.4 kg of active ingredients per ha, farms with a high level of land use intensity sprayed on average 16 times and used 22.0 kg/ha. They also used a greater number of different products and tended to mix them together. The intensity of pesticide use was particularly high for cash crops such as tomatoes, chilies and strawberries. Many farmers experienced health problems related to pesticide use because pesticides were not correctly handled. Greater investment is needed in the development of integrated pest management in the long-term, and health problems may be reduced in the short-term by raising awareness among farmers regarding the risks they are exposing themselves to, as well as by promoting good agricultural practices.

We report on the response of liquid xenon to low energy electronic recoils below 15 keV from beta decays of tritium at drift fields of 92 V/cm, 154 V/cm and 366 V/cm using the XENON100 detector. A data-to-simulation fitting method based on Markov Chain Monte Carlo is used to extract the photon yields and recombination fluctuations from the experimental data. The photon yields measured at the two lower fields are in agreement with those from literature; additional measurements at a higher field of 366 V/cm are presented. The electronic and nuclear recoil discrimination as well as its dependence on the drift field and photon detection efficiency are investigated at these low energies. The results provide new measurements in the energy region of interest for dark matter searches using liquid xenon.

The transformation of agriculture in lower income countries from subsistence-to market-oriented production systems has important implications for farmers' risk exposure and risk management yet only few studies have paid attention to this. This paper fills this gap and particularly focuses on the role of pesticides in managing the risk from crop pests and diseases, which is major source of risk to farmers. Data were collected for 240 Thai upland farmers stratified by ten levels of agricultural commercialization. The results show that risk perceptions and management strategies are strongly associated with levels of agricultural commercialization. Key strategies for commercial farmers included monitoring market prices, diversifying sales channels and applying large quantities of pesticides, while crop diversification and debt avoidance were more important for subsistence-oriented farmers. High levels of pesticide use at commercial farms were not accompanied by a safer handling practices, as farmers largely neglected pesticide health risks. The results point at the importance of tailored agricultural policies to strengthen farmers' resilience against risk at varying levels of commercialization, rather than following a one size fits all approach.

Abstract Voluntary sustainability standards, aimed at improving the environmental, social and economic aspects of agricultural production and trade, are becoming increasingly common. The coffee sector is a prime example, where sustainability certification could improve livelihoods for poor smallholders. However, as individual production volumes are low, smallholder farmers need to cooperate in certification as a group, which makes impact assessment more complicated. Previous empirical studies, reporting premia of up to 30%, have neglected the costs associated with group certification. We explore the issue using an agent‐based simulation of coffee producer organisations in Uganda, including the certification‐related costs for farmers. Our results suggest that certification can have a small positive impact on participating households. But the added value of certification is substantially lower than the price premium, because of certification costs. Increasing both the membership of the producer groups and their deliveries of certified coffee are necessary to improve the rewards of certification.

A 220 Rn source is deployed on the XENON100 dark matter detector in order to address the challenges in calibration of tonne-scale liquid noble element detectors.We show that the 212 Pb beta emission can be used for low-energy electronic recoil calibration in searches for dark matter.The isotope spreads throughout the entire active region of the detector, and its activity naturally decays below background level within a week after the source is closed.We find no increase in the activity of the troublesome 222 Rn background after calibration.Alpha emitters are also distributed throughout the detector and facilitate calibration of its

We present results of searches for vector and pseudoscalar bosonic super-weakly interacting massive particles (WIMPs), which are dark matter candidates with masses at the keV-scale, with the XENON100 experiment. XENON100 is a dual-phase xenon time projection chamber operated at the Laboratori Nazionali del Gran Sasso. A profile likelihood analysis of data with an exposure of 224.6 live days $\ifmmode\times\else\texttimes\fi{}34\text{ }\text{ }\mathrm{kg}$ showed no evidence for a signal above the expected background. We thus obtain new and stringent upper limits in the $(8--125)\text{ }\text{ }\mathrm{keV}/{\mathrm{c}}^{2}$ mass range, excluding couplings to electrons with coupling constants of ${g}_{ae}&gt;3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}13}$ for pseudo-scalar and ${\ensuremath{\alpha}}^{\ensuremath{'}}/\ensuremath{\alpha}&gt;2\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}28}$ for vector super-WIMPs, respectively. These limits are derived under the assumption that super-WIMPs constitute all of the dark matter in our galaxy.

We present first results on the scalar coupling of weakly interacting massive particles (WIMPs) to pions from 1 t yr of exposure with the XENON1T experiment. This interaction is generated when the WIMP couples to a virtual pion exchanged between the nucleons in a nucleus. In contrast to most nonrelativistic operators, these pion-exchange currents can be coherently enhanced by the total number of nucleons and therefore may dominate in scenarios where spin-independent WIMP-nucleon interactions are suppressed. Moreover, for natural values of the couplings, they dominate over the spin-dependent channel due to their coherence in the nucleus. Using the signal model of this new WIMP-pion channel, no significant excess is found, leading to an upper limit cross section of 6.4×10^{-46} cm^{2} (90% confidence level) at 30 GeV/c^{2} WIMP mass.

Abstract In this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.

The radiation environment at the surface of Mars is mainly dominated by incoming galactic cosmic rays (GCRs) that propagate through the atmosphere, with sporadic strong contributions from solar energetic particles (SEPs). The main driver for changes in the radiation field, on time scales of years, is the solar modulation of the GCR flux. During times of higher solar activity, GCRs are more strongly attenuated, resulting in highest GCR fluxes during solar minimum and lowest fluxes at solar maximum. We report dosimetric measurements conducted with the Radiation Assessment Detector (RAD) from November 2019 to October 2020 during the recent deep solar minimum. RAD has been operating on board NASA's Curiosity rover on Mars since August 2012. We bring these measurements into context with RAD measurements from 2012 to 2013 around the (weak) maximum of Solar Cycle 24. The results show the impact of the changing solar modulation from 2012 to 2020 on the Martian surface radiation environment and have implications for future human exploration missions of Mars. We find that while the overall radiation dose rate has increased significantly by 50% between the two time frames, the biologically highly relevant dose equivalent rate shows a modest increase of 13%, yielding interesting input for the timing of such Mars missions within the solar cycle. We also report the first results of the analysis of the flux of medium-energy protons with 100–300 MeV on the Martian surface, yielding an important additional, in-situ measured data point for validating radiation transport models.

Abstract. We present a comprehensive, high-quality dataset characterizing soil–vegetation and land surface processes from continuous measurements conducted in two climatically contrasting study regions in southwestern Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 ∘C and annual precipitation of 890 mm and the cooler and wetter Swabian Alb with mean temperature 7.5 ∘C and annual precipitation of 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance (EC) stations which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site years containing observations with a multitude of crops, as well as considerable variation in local growing-season climates. The presented multi-site, multi-year dataset is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass, and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30 min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1200 soil samples were taken to study changes of carbon and nitrogen contents. The dataset is available at https://doi.org/10.20387/bonares-a0qc-46jc (Weber et al., 2021). One field in each region is still fully set up as continuous observatories for state variables and fluxes in intensively managed agricultural fields.

Galactic cosmic radiation and secondary particles produced in the interaction with the atmosphere lead to a complex radiation field on the Martian surface. A workshop (“1st Mars Space Radiation Modeling Workshop”) organized by the MSL-RAD science team was held in June 2016 in Boulder with the goal to compare models capable to predict this radiation field with each other and measurements from the RAD instrument onboard the curiosity rover taken between November 15, 2015 and January 15, 2016. In this work the results of PLANETOCOSMICS/GEANT4 contributed to the workshop are presented. Calculated secondary particle spectra on the Martian surface are investigated and the radiation field's directionality of the different particles in dependence on the energy is discussed. Omnidirectional particle fluxes are used in combination with fluence to dose conversion factors to calculate absorbed dose rates and dose equivalent rates in a slab of tissue.

Farmers in the State of Mato Grosso are among Brazil's most productive soybean, maize and cotton producers, but are still far away from achieving potential yields as measured on experimental sites. The objective of this study was to decompose yield gaps in the Southern Amazon into their biophysical and socio-economic dimensions. In order to achieve this, the process-based MOdel of NItrogen and Carbon dynamics in Agro-ecosystems (MONICA) was coupled with the Mathematical Programming-based Multi-Agent Systems (MPMAS) software. Soybean, maize and cotton yield gaps were simulated for five macro-regions in Mato Grosso considering different climatic, edaphic and crop management conditions. The impact of socio-economic constraints on crop yields was assessed in form of full factorial design in which each factor was set to a baseline and unconstrained level. The simulation results show that biophysical yield gaps (due to water and nutrient deficit) account for 24% of potential yields (Yp), whereas an unrestricted access to machinery, labour, credit and technological innovation would lead to a reduction of yield gaps by 6.1% and an expansion of cropland by 22%. Yield gaps can be reduced through improved water- and nutrient management, appropriate cultivar-sowing date combinations and in part by a removal of socio-economic constraints. However, each solution comes with its own limitation either in form of increased pressure on limited environmental resources or incompatibility with individual farmer objectives. Future yield gap closure will depend on the access to arable land, environmental regulations preventing further deforestation as well as political and economic incentives for sustainable intensification.

The XENON1T liquid xenon time projection chamber is the most sensitive detector built to date for the measurement of direct interactions of weakly interacting massive particles with normal matter. The data acquisition system (DAQ) is constructed from commercial, open source, and custom components to digitize signals from the detector and store them for later analysis. The system achieves an extremely low signal threshold by triggering each channel independently, achieving a single photoelectron acceptance of (93 ± 3)%, and deferring the global trigger to a later, software stage. The event identification is based on MongoDB database queries and has over 98% efficiency at recognizing interactions at the analysis threshold in the center of the target. A readout bandwidth over 300 MB/s is reached in calibration modes and is further expandable via parallelization. This DAQ system was successfully used during three years of operation of XENON1T.

Understanding the zenith angle dependence of the Martian surface radiation environment is crucial for planning future human exploration missions to Mars. In our previous research (Wimmer et al. 2015; Guo et al. 2021; Khaksarighiri et al. 2023) we extensively studied the zenith-angle dependence of the Martian surface radiation dose rate. Leveraging the same validated radiation model, calibrated with data from the Radiation Assessment Detector (RAD) on Mars, we calculated the flux of secondary downward particles reaching to the surface of Mars from various zenith angles resulting from the interaction of primary particles with the Martian atmosphere.&amp;#160; These flux of secondary particles, coming from different zenith angles, can be integrated into a comprehensive topographic map of Mars, providing a detailed depiction of the global radiation landscape.The construction of this radiation map requires careful consideration of various factors, including atmospheric column density, local and large-scale topography offering potential shielding effects, and the input spectrum is affected by heliospheric modulation. Additionally, accounting for seasonal pressure cycles and daily atmospheric surface pressure due to thermal tides is essential. Our model specifically focused on the influence of zenith angle on atmospheric column depth and simulations tailored to the Gale Crater region, a region explored by the Curiosity rover.&amp;#160; Applying this methodology allows us to create lookup tables of all secondary particles reaching the Martian surface from various zenith angles and evaluate the atmospheric impact. Employing these matrices alongside the incident spectrum enables the calculation of secondary particle flux from all zenith angles on the Martian surface. This method provides valuable insights into the fluctuations in radiation flux on Mars, facilitating thorough assessments of potential radiation hazards. Mission planners can leverage these data, obtaining vital information to identify secure landing areas and sheltered regions for astronauts on the Martian surface.

The Radiation Assessment Detector (RAD) onboard the Mars Science Laboratory's Curiosity rover is the first-ever instrument continuously monitoring energetic particles on the surface of Mars. Since the rover's landing on August 6, 2012, RAD has accumulated valuable data, providing an unprecedented opportunity to assess the radiation environment across a solar cycle on an another planet.Understanding the radiation environment on Mars is crucial for a more accurate assessment of the risks posed to manned future space missions. Moreover, it also serves to further investigate planetary conditions, properties of the Sun, and galactic cosmic rays (GCRs).&amp;#160;&amp;#160;The radiation field on the surface of Mars primarily consists of charged particles, including primary GCRs propagating to the Martian surface and secondary particles generated through the interaction of primary GCRs with the Martian atmosphere or soil.&amp;#160;Furthermore, it undergoes temporal changes caused by factors such as atmospheric pressure variations due to thermal tides, seasonal changes, geographical and topographical shielding effects, heliospheric modulation of GCRs, as well as Martian soil and subsurface conditions. Considering all these factors is essential for a comprehensive description of the radiation environment.&amp;#160;&amp;#160;Here we utilize the extensive RAD dataset spanning the last 11 years to delve into the intricate variations in particle flux. Our analysis encompasses a diverse array of particle species, providing a comprehensive understanding of how particle flux evolves over the course of one complete solar cycle. This extended time frame allows us to capture and analyze long-term trends, offering valuable insights into the dynamic nature of particle interactions within the Martian environment. By exploring the temporal patterns of particle flux across different species, we aim to contribute to a more nuanced comprehension of the complex radiation dynamics on Mars and its implications for future space missions and potential habitation.&amp;#160;&amp;#160;Additionally, we endeavored to understand the impacts of subsurface composition on the Martian surface radiation field, particularly in generating additional upward particles. This investigation is significant as it contributes to the exploration of potential subsurface water content on the surface of Mars.

In this paper, we describe the XENON100 data analyses used to assess the target-intrinsic background sources radon ( ), thoron ( ) and krypton ( ). We detail the event selections of high-energy alpha particles and decay-specific delayed coincidences. We derive distributions of the individual radionuclides inside the detector and quantify their abundances during the main three science runs of the experiment over a period of $$\sim 4\,\hbox {years}$$ , from January 2010 to January 2014. We compare our results to external measurements of radon emanation and krypton concentrations where we find good agreement. We report an observed reduction in concentrations of radon daughters that we attribute to the plating-out of charged ions on the negatively biased cathode.

Two-neutrino double electron capture is a rare nuclear decay where two electrons are simultaneously captured from the atomic shell. For $^{124}$Xe this process has not yet been observed and its detection would provide a new reference for nuclear matrix element calculations. We have conducted a search for two-neutrino double electron capture from the K-shell of $^{124}$Xe using 7636 kg$\cdot$d of data from the XENON100 dark matter detector. Using a Bayesian analysis we observed no significant excess above background, leading to a lower 90 % credibility limit on the half-life $T_{1/2}>6.5\times10^{20}$ yr. We also evaluated the sensitivity of the XENON1T experiment, which is currently being commissioned, and find a sensitivity of $T_{1/2}>6.1\times10^{22}$ yr after an exposure of 2 t$\cdot$yr.

Pesticide application rates are high and increasing in upland agricultural systems in Thailand producing vegetables, fruits and ornamental crops, leading to the pollution of stream water with pesticide residues. The objective of this study was to determine the maximum per hectare application rates of two widely used pesticides that would achieve non-hazardous pesticide concentrations in the stream water and to evaluate how farm household incomes would be affected if farmers complied with these restricted application rates. For this purpose we perform an integrated modeling approach of a hydrological solute transport model (the Soil and Water Assessment Tool, SWAT) and an agent-based farm decision model (Mathematical Programming-based Multi-Agent Systems, MPMAS). SWAT was used to simulate the pesticide fate and behavior. The model was calibrated to a 77 km(2) watershed in northern Thailand. The results show that to stay under a pre-defined eco-toxicological threshold, the current average application of chlorothalonil (0.80 kg/ha) and cypermethrin (0.53 kg/ha) would have to be reduced by 80% and 99%, respectively. The income effect of such reductions was simulated using MPMAS. The results suggest that if farm households complied with the application thresholds then their income would reduce by 17.3% in the case of chlorothalonil and by 38.3% in the case of cypermethrin. Less drastic income effects can be expected if methods of integrated pest management were more widely available. The novelty of this study is to combine two models from distinctive disciplines to evaluate pesticide reduction scenarios based on real-world data from a single study site.

Combinations of factors, including inappropriate economic policies, have contributed to the poor economic performance of sub-Saharan Africa (SSA). The impacts of some corrective policy measures, both on the macro economy and on the rural economy, are not very clear because they have led to unintended consequences, such as increasing poverty and inequality. This paper examines the effect of the removal of subsidised agricultural credit for irrigation farmers in Ghana, a country of pioneering reforms in SSA. A theoretical model of this scenario is constructed, in which it is shown that under multiple-market imperfections farmers resort to alternative income sources to finance irrigation. Particularly in the presence of off-farm alternatives, multiple-market imperfections can induce both on- and off-farm income-generating activities during the same season. This model is subsequently tested and validated with household data collected from northern Ghana. The empirical analysis shows that there is a strong complementarity between irrigation farming and off-farm employment, two activities that depend heavily on labour endowment. The observed complementarity suggests that in weak credit markets irrigation farmers generate liquidity from off-farm activities, which could lead to a demand for larger family size in the long run.

Abstract As the consequences of climate change for agricultural production slowly unfold at the local level (sometimes with contradicting signals), farmers’ information processing and decision making become more relevant for policy analysis and modelling. The major challenge is to reveal patterns in the way farmers form expectations about future production outcomes and to encode these findings into models of heterogeneous expectation formation. We developed and tested a payout‐motivated field experiment to observe farmer decision‐making under climate change and to examine how they form their expectations in a recursive‐dynamic context. Participants were exposed to ambiguity and acquired incremental evidence about the true distribution of possible climate outcomes through repeated random draws. Simulation models used in agricultural and environmental research usually implement simple forms of adaptive agent expectation or completely neglect this issue by assuming perfect foresight or constant expectations. Our computer laboratory experiments with blue‐ and white‐collar farmers from Southwest Germany ( n = 97) suggest that expectation behaviour of a large share of farmers can be well replicated with Bayesian types of expectation models.

Agricultural production plays an essential role in food security and economic development, but given its direct links within the environment, it is also an important driver of environmental degradation. It has become essential to not only produce more crops but doing it while maintaining or reducing the respective environmental impacts. A promising method for evaluating production efficiency is the nonparametric eco-efficiency analysis, which compares the economic value added against a composite environmental pressure indicator. This article proposes a novel method of evaluating the eco-efficiency scores, which does not depend on field survey data, but rather on multi-agent simulations. We present the first estimates of eco-efficiency for crop farms in the Amazon and Cerrado biomes in Brazil, identify regions and farm profiles that could be the focus of targeted interventions, and evaluate whether eco-efficiency scores could be improved using an alternative scenario. We combine a biophysical model with bioeconomic agent-based simulations to mimic land-use decisions of real-world farms. We then estimate the efficiency scores with an enhanced order-m estimator that conditions the efficiency estimates on explanatory variables, thus producing robust efficiency measures. Our simulations reveal that there are indeed differences in eco-efficiency estimates between macro-regions in the federal state of Mato Grosso. According to our simulations, the Southeast exhibited the greatest occurrences of inefficiencies, followed by the West macro-region. In our life-cycle inventory, sunflower cultivation had the lowest levels of environmental pressures. However, when evaluating it in a prospective scenario of infrastructure development, we could not observe a positive impact on efficiency. By using efficient computational methods, we replicate our simulations many times to create robust estimates that are more representative than a single field survey. In addition, our novel method combines simulated farm data with eco-efficiency analyses, allowing ex-ante impact evaluations where policy interventions can be tested before their implementation.

Abstract Surrogate modeling can overcome computational and data-privacy constraints of micro-scale economic models and support their incorporation into large-scale simulations and interactive simulation experiments. We compare four data-driven methods to reproduce the aggregated crop area response simulated by farm-level modeling in response to price variation. We use the isometric log-ratio transformation to accommodate the compositional nature of the output and sequential sampling with stability analysis for efficient model selection. Extreme gradient boosting outperforms multivariate adaptive regressions splines, random forest regression, and classical multinomial-logistic regression and achieves high goodness-of-fit from moderately sized samples. Explicitly including ratio terms between price input variables considerably improved prediction, even for highly automatic machine learning methods that should in principle be able to detect such input variable interaction automatically. The presented methodology provides a solid basis for the use of surrogate modeling to support the incorporation of micro-scale models into large-scale integrated simulations and interactive simulation experiments with stakeholders.

Abstract Current approaches to project spatial biodiversity responses to climate change mainly focus on the direct effects of climate on species while regarding land use and land cover as constant or prescribed by global land‐use scenarios. However, local land‐use decisions are often affected by climate change and biodiversity on top of socioeconomic and policy drivers. To realistically understand and predict climate impacts on biodiversity, it is, therefore, necessary to integrate both direct and indirect effects (via climate‐driven land‐use change) of climate change on biodiversity. In this perspective paper, we outline how biodiversity models could be better integrated with regional, climate‐driven land‐use models. We initially provide a short, non‐exhaustive review of empirical and modelling approaches to land‐use and land‐cover change (LU) and biodiversity (BD) change at regional scales, which forms the base for our perspective about improved integration of LU and BD models. We consider a diversity of approaches, with a special emphasis on mechanistic models. We also look at current levels of integration and at model properties, such as scales, inputs and outputs, to further identify integration challenges and opportunities. We find that LU integration in BD models is more frequent than the other way around and has been achieved at different levels: from overlapping predictions to simultaneously coupled simulations (i.e. bidirectional effects). Of the integrated LU‐BD socio‐ecological models, some studies included climate change effects on LU, but the relative contribution of direct vs. indirect effects of climate change on BD remains a key research challenge. Important research avenues include concerted efforts in harmonizing spatial and temporal resolution, disentangling direct and indirect effects of climate change on biodiversity, explicitly accounting for bidirectional feedbacks, and ultimately feeding socio‐ecological systems back into climate predictions. These avenues can be navigated by matching models, plugins for format and resolution conversion, and increasing the land‐use forecast horizon with adequate uncertainty. Recent developments of coupled models show that such integration is achievable and can lead to novel insights into climate–land use–biodiversity relations. Read the free Plain Language Summary for this article on the Journal blog.

To keep yield advances, farmers in Mato Grosso (MT) have been adopting several technological innovations. Therefore, agricultural production systems in MT have become complex and dynamic since farmers have to consider the increase of decision variables when planning and implementing their farming practices. These variables are widely spread across many distinct topics, bringing them together and summarizing information from diverse fields of research has become a difficult task in farmers’ decision-making process. Therefore, we performed an Integrated Assessment simulation experiment with a region-specific bio-economic component to assess trade-offs between different agricultural practices in a double cropping system. The simulation experiment was carried out with MPMAS, a multi-agent software package developed for simulating farm-based economic behavior and human-environment interactions in agriculture. Crop yields were simulated with the Model of Nitrogen and Carbon dynamics in Agro-ecosystems (MONICA). Our simulation results show a trade-off between lower soybean yields with the flexibility of double cropping when soybean with shorter maturity cycle is introduced. Results also captured regional differences in terms of land use share of different crops and farm configurations of double cropping. These results provide key insights into a farmer’s decision-making process depending on a multitude of decision variables.

Radiation exposure of aircrew is a serious concern which has been given special emphasis in the European Council directive 96/29/Euratom. The cosmic ray induced neutron component can contribute more than 50% to the biologically relevant dose at aviation altitudes. Various computational approaches to route dose assessment, e.g. CARI, are in use nowadays and are compared with experimental data. Measurements of aircrew exposure usually involve extensive instrumentation in order to cover the whole particle spectrum and energy range present inside aircraft. Due to their small size and easy handling, thermoluminescence dosemeters represent an appropriate alternative. Previous measurements onboard aircraft applying the high-temperature ratio method with LiF:Mg,Ti dosemeters for the determination of an 'averaged' linear energy transfer of mixed radiation fields demonstrate the ability of this method to evaluate the dose equivalent, according to the Q(LETinfinity) relationship proposed by the ICRP. Measurements with CaF2:Tm dosemeters are currently in progress and are discussed here.

This paper presents a spatial multi-agent programming model, which has been developed for assessing policy options in the diffusion of innovations and resource use changes. Unlike conventional simulation tools used in agricultural economics, the model class described here applies a multi-agent/cellular automata (CA) approach by using heterogeneous farm-household models and capturing their social and spatial interactions explicitly. The individual choice of the farm-household among available production, consumption, investment and marketing alternatives is represented in recursive linear programming models. Adoption constraints are introduced in form of network-threshold values that reflect the cumulative effects of experience and observation of peers’ experiences. The model's economic and hydrologic components are tightly connected into a spatial framework. The integration of economic and hydrologic processes facilitates the consideration of feedback effects in the use of water for irrigation. The simulation runs of the model are carried out with an empirical data set, which has been derived from various data sources on an agricultural region in Chile. Simulation results show that agent-based spatial modelling constitutes a powerful approach to better understanding processes of innovation and resource use change.

A change in land use from the growing of upland rice to the cultivation of cash crops has increased the level of use of synthetic pesticides in the mountainous areas of Thailand and Vietnam. Although this increase has occurred generally across both countries, it has been especially prevalent in mountainous areas. The objective of this chapter is to describe the challenges faced when wishing to reduce the risks caused by the use of agricultural pesticides in mountainous areas, both from an economic and a biophysical point of view. Building on case studies from Thailand and Vietnam, we show how the potential risk of pesticide use is related to the limited experience farmers have in handling pesticides, and the hydrological relationships between highland and lowland areas.

We present the first search for dark matter-induced delayed coincidence signals in a dual-phase xenon time projection chamber, using the 224.6 live days of the XENON100 science run II. This very distinct signature is predicted in the framework of magnetic inelastic dark matter which has been proposed to reconcile the modulation signal reported by the DAMA/LIBRA collaboration with the null results from other direct detection experiments. No candidate event has been found in the region of interest and upper limits on the WIMP's magnetic dipole moment are derived. The scenarios proposed to explain the DAMA/LIBRA modulation signal by magnetic inelastic dark matter interactions of WIMPs with masses of 58.0 GeV/c$^2$ and 122.7 GeV/c$^2$ are excluded at 3.3 $\sigma$ and 9.3 $\sigma$, respectively.

A water-filled tissue equivalent phantom with a diameter of 35 cm was developed at the Institute for Biomedical Problems, Moscow, Russia. It contains four channels perpendicular to each other, where dosemeters can be exposed at different depths. Between May 1997 and February 1999 the phantom was installed at three different locations on board the Mir space station. ThermoluminescenCE dosemeters (TLDs) were exposed at various depths inside the phantom either parallel or perpendicular to the hull of the spacecraft. The high-temperature ratio (HTR) method was used for the evaluation of the TLDs. The method was developed at the Atominstitute of the Austrian Universities, Vienna, Austria, and has already been used for measurements in mixed radiation fields on earth and in space with great success. It uses the changes of peak height ratios in LiF:Mg,Ti glow curves in dependence on the linear energy transfer (LET), and therefore allows determination of an 'averaged' LET as well as measurement of the absorbed dose. A mean quality factor and, subsequently, the dose equivalent can be calculated according to the Q(LET() relationship proposed by the ICRP. The small size of the LiF dosemeters means that the HTR method can be used to determine the gradient of absorbed dose and dose equivalent inside the tissue equivalent body.

The exposure of aircrew personnel to cosmic radiation has been considered as occupational exposure in the European Union since the European Council Directive 96/26/EURATOM became effective on 13th May 1996. In Germany the corresponding safety standards for aircrew are regulated by the German Radiation Protection Ordinance, which implemented the European law in 2001. The radiation exposure of the flight crew of the LUFTHANSA group is calculated by the DLR Institute of Aerospace Medicine in Cologne, applying the calculation program EPCARD in the framework of the aircrew dose determination system CALculated and Verified Aviation DOSimetry (CALVADOS). Besides the operational dose calculations, DLR performs measurements at airflight altitudes using active (e.g. TEPC, DOSTEL, etc.) and passive (Thermoluminescence detectors (TLDs), bubble detectors) radiation detectors to verify the calculation codes. Within these activities the project BOdy DOsimetry (BODO) comprised a long-term exposure of a RANDO© anthropomorphic phantom to measure the skin and the depth dose distribution inside a human torso applying TLDs at aviation altitudes for the first time. The torso was flown onboard a LUFTHANSA Cargo aircraft for 3 months from mid of July to mid of October 2004. Over 800 TLDs were positioned for depth dose measurements in the head, the thorax and the abdomen of the torso. In addition dosemeter packages have been distributed on the surface of the torso to measure the skin dose as well as in the transport container and on the flight deck.

In recent years, many scholars praised the seemingly endless possibilities of using machine learning (ML) techniques in and for agent-based simulation models (ABM). To get a more comprehensive understanding of these possibilities, we conduct a systematic literature review (SLR) and classify the literature on the application of ML in and for ABM according to a theoretically derived classification scheme. We do so to investigate how exactly machine learning has been utilized in and for agent-based models so far and to critically discuss the combination of these two promising methods. We find that, indeed, there is a broad range of possible applications of ML to support and complement ABMs in many different ways, already applied in many different disciplines. We see that, so far, ML is mainly used in ABM for two broad cases: First, the modelling of adaptive agents equipped with experience learning and, second, the analysis of outcomes produced by a given ABM. While these are the most frequent, there also exist a variety of many more interesting applications. This being the case, researchers should dive deeper into the analysis of when and how which kinds of ML techniques can support ABM, e.g. by conducting a more in-depth analysis and comparison of different use cases. Nonetheless, as the application of ML in and for ABM comes at certain costs, researchers should not use ML for ABMs just for the sake of doing it.

Abstract Zielsetzung der Risiko‐Aggregation ist die auf die Risiko‐Analyse aufbauende Bestimmung des Gesamtrisikoumfangs. Mit der Monte‐Carlo‐Simulation als wichtigstes Verfahren der Risiko‐Aggregation wird anhand eines Fallbeispiels erklärt, wie Risiken aggregiert und daraus Eigenkapitalbedarf, Rating und Kapitalkostensätze bestimmt werden können.

Sustainable development requires a mix of policies that can simultaneously address social, economic and environmental objectives. While the preceding chapters of this book have focused on agricultural, environmental and socio-economic aspects and related policies, this chapter looks at the commercialization of smallholder agriculture and, in particular, the need to target the poor so as to enable them to better participate in market-oriented development. The mountainous regions of northern Thailand and northern Vietnam have witnessed a substantial transformation over the last two decades, turning as they have from largely subsistence-oriented to market-oriented agriculture. This development began in Thailand earlier than in Vietnam, but during the 2000s, smallholder agriculture in Vietnam also commercialized at a rapid rate, leading to an increase in farm incomes and a decline in poverty levels. Our main policy conclusion here is that the commercialization of agriculture can be conducive to a sustainable increase in smallholder incomes and reduction of poverty levels; however, policies aimed at addressing the environmental externalities caused by market participation must be combined with socially-oriented policies that target poorer segments of the population, especially in the areas of education, health, social assistance, political participation and non-subsidized credit, as well as infrastructure and market-oriented development policies aimed at long-run sustainability.

One key advantage of agent-based modeling (ABM) is the one-to-one correspondence of real-world and computational agents, which facilitates participatory simulation and model-enhanced learning. Using ABM effectively poses a number of challenges that have not been fully resolved yet. Some of these challenges relate to organizational and institutional factors, such as finding an appropriate boundary arrangement in which scientists, policy makers and stakeholders can interact and jointly make use of the models. Other challenges relate to technical and economic factors, as the models must ensure continuous stakeholder involvement and actually provide some returns to end-users. This research tested computer-based decision tools in a knowledge broker arrangement. We applied the MP-MAS software to simulate how farmers interact with each other and react to changes in their economic and natural environment. In particular, we used the model for evaluating the willingness-to-pay for the construction of a new reservoir. A key innovation of the research was the development of the decision-support tools in close interaction with multiple stakeholders, including water user associations and members of the irrigation and agricultural administration. This interaction, which was organized in the form of individual consultations, workshops and training sessions, ensured that the simulations addressed the needs and priorities of different stakeholders and took their local knowledge into account.