Adaptation options under climate change for multifunctional agriculture: a simulation study for western Switzerland

Besides its primary role in producing food and fiber, agriculture also has relevant effects on several other functions, such as management of renewable natural resources. Climate change (CC) may lead to new trade-offs between agricultural functions or aggravate existing ones, but suitable agricultural management may maintain or even improve the ability of agroecosystems to supply these functions. Hence, it is necessary to identify relevant drivers (e.g., cropping practices, local conditions) and their interactions, and how they affect agricultural functions in a changing climate. The goal of this study was to use a modeling framework to analyze the sensitivity of indicators of three important agricultural functions, namely crop yield (food and fiber production function), soil erosion (soil conservation function), and nutrient leaching (clean water provision function), to a wide range of agricultural practices for current and future climate conditions. In a two-step approach, cropping practices that explain high proportions of variance of the different indicators were first identified by an analysis of variance-based sensitivity analysis. Then, most suitable combinations of practices to achieve best performance with respect to each indicator were extracted, and trade-offs were analyzed. The procedure was applied to a region in western Switzerland, considering two different soil types to test the importance of local environmental constraints. Results show that the sensitivity of crop yield and soil erosion due to management is high, while nutrient leaching mostly depends on soil type. We found that the influence of most agricultural practices does not change significantly with CC; only irrigation becomes more relevant as a consequence of decreasing summer rainfall. Trade-offs were identified when focusing on best performances of each indicator separately, and these were amplified under CC. For adaptation to CC in the selected study region, conservation soil management and the use of cropped grasslands appear to be the most suitable options to avoid trade-offs

Adapting agricultural land management to climate change: a regional multi-objective optimization approach

In several regions of the world, climate change is expected to have severe impacts on agricultural systems. Changes in land management are one way to adapt to future climatic conditions, including land-use changes and local adjustments of agricultural practices. In previous studies, options for adaptation have mostly been explored by testing alternative scenarios. Systematic explorations of land management possibilities using optimization approaches were so far mainly restricted to studies of land and resource management under constant climatic conditions. In this study, we bridge this gap and exploit the benefits of multi-objective regional optimization for identifying optimum land management adaptations to climate change. We design a multi-objective optimization routine that integrates a generic crop model and considers two climate scenarios for 2050 in a meso-scale catchment on the Swiss Central Plateau with already limited water resources. The results indicate that adaptation will be necessary in the study area to cope with a decrease in productivity by 0-10%, an increase in soil loss by 25-35%, and an increase in N-leaching by 30-45%. Adaptation options identified here exhibit conflicts between productivity and environmental goals, but compromises are possible. Necessary management changes include (i) adjustments of crop shares, i.e. increasing the proportion of early harvested winter cereals at the expense of irrigated spring crops, (ii) widespread use of reduced tillage, (iii) allocation of irrigated areas to soils with low water-retention capacity at lower elevations, and (iv) conversion of some pre-alpine grasslands to cropland

Running of Neutrino Parameters and the Higgs Self-Coupling in a Six-Dimensional UED Model

We investigate a six-dimensional universal extra-dimensional model in the extension of an effective neutrino mass operator. We derive the \beta-functions and renormalization group equations for the Yukawa couplings, the Higgs self-coupling, and the effecive neutrino mass operator in this model. Especially, we focus on the renormalization group running of physical parameters such as the Higgs self-coupling and the leptonic mixing angles. The recent measurements of the Higgs boson mass by the ATLAS and CMS collaborations at the LHC as well as the current three-flavor global fits of neutrino oscillation data have been taken into account. We set a bound on the six-dimensional model, using the vacuum stability criterion, that allows five Kaluza-Klein modes only, which leads to a strong limit on the cutoff scale. Furthermore, we find that the leptonic mixing angle \theta_{12} shows the most sizable running, and that the running of the angles \theta_{13} and \theta_{23} are negligible. Finally, it turns out that the findings in this six-dimensional model are comparable with what is achieved in the corresponding five-dimensional model, but the cutoff scale is significantly smaller, which means that it could be detectable in a closer future.Comment: 14 pages, 3 figures. Final version published in Phys. Lett.

Renormalization Group Running of the Neutrino Mass Operator in Extra Dimensions

We study the renormalization group (RG) running of the neutrino masses and the leptonic mixing parameters in two different extra-dimensional models, namely, the Universal Extra Dimensions (UED) model and a model, where the Standard Model (SM) bosons probe an extra dimension and the SM fermions are confined to a four-dimensional brane. In particular, we derive the beta function for the neutrino mass operator in the UED model. We also rederive the beta function for the charged-lepton Yukawa coupling, and confirm some of the existing results in the literature. The generic features of the RG running of the neutrino parameters within the two models are analyzed and, in particular, we observe a power-law behavior for the running. We note that the running of the leptonic mixing angle \theta_{12} can be sizable, while the running of \theta_{23} and \theta_{13} is always negligible. In addition, we show that the tri-bimaximal and the bimaximal mixing patterns at a high-energy scale are compatible with low-energy experimental data, while a tri-small mixing pattern is not. Finally, we perform a numerical scan over the low-energy parameter space to infer the high-energy distribution of the parameters. Using this scan, we also demonstrate how the high-energy \theta_{12} is correlated with the smallest neutrino mass and the Majorana phases.Comment: 20 pages, 5 figures, REVTeX4-1. (v2) Final version published in J. High Energy Phys. (v3) A short clarification at the end of the appendix has been adde

Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions

agricultur

Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions

ISSN:2077-047

Adaptation options under climate change for multifunctional agriculture: a simulation study for western Switzerland

Besides its primary role in producing food and fiber, agriculture also has relevant effects on several other functions, such as management of renewable natural resources. Climate change (CC) may lead to new trade-offs between agricultural functions or aggravate existing ones, but suitable agricultural management may maintain or even improve the ability of agroecosystems to supply these functions. Hence, it is necessary to identify relevant drivers (e.g., cropping practices, local conditions) and their interactions, and how they affect agricultural functions in a changing climate. The goal of this study was to use a modeling framework to analyze the sensitivity of indicators of three important agricultural functions, namely crop yield (food and fiber production function), soil erosion (soil conservation function), and nutrient leaching (clean water provision function), to a wide range of agricultural practices for current and future climate conditions. In a two-step approach, cropping practices that explain high proportions of variance of the different indicators were first identified by an analysis of variance-based sensitivity analysis. Then, most suitable combinations of practices to achieve best performance with respect to each indicator were extracted, and trade-offs were analyzed. The procedure was applied to a region in western Switzerland, considering two different soil types to test the importance of local environmental constraints. Results show that the sensitivity of crop yield and soil erosion due to management is high, while nutrient leaching mostly depends on soil type. We found that the influence of most agricultural practices does not change significantly with CC; only irrigation becomes more relevant as a consequence of decreasing summer rainfall. Trade-offs were identified when focusing on best performances of each indicator separately, and these were amplified under CC. For adaptation to CC in the selected study region, conservation soil management and the use of cropped grasslands appear to be the most suitable options to avoid trade-offs

webXTREME: R-based web tool for calculating agroclimatic indices of extreme events

We document the release of webXTREME, a new online tool for the evaluation of indices of climatic extremes (extreme temperatures and aridity) having impact on agricultural production. The tool is globally available and can be operated with either observed weather data or time series representing future climatic conditions. It is thus suitable for risk evaluation under climate change. webXTREME was implemented using Shiny, an open-source programming framework for creating web applications on the basis of the R Statistical Language