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Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO 2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO 2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO 2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management., (© 2020 John Wiley & Sons Ltd.)

Water deficit and water excess constitute severe stresses that limit crop yield and are likely to intensify as climate becomes more variable. Regional crop production aggregates for the US Midwest indicate widespread yield losses in past decades due to both extreme rainfall and water limited conditions, though the degree to which these weather impacts are related to site-specific factors such as landscape position and soils has not been examined in a systematic manner. This study offers observational evidence from a large sample of commercial crop fields to support the hypothesis that landscape position is the primary mediator of crop yield responses to weather within unstable field zones (i.e., zones where yields tend to fluctuate between high and low, depending on the year). Results indicate that yield losses in unstable zones driven by water excess and deficits occur throughout a wide range of seasonal rainfall, even simultaneously under normal weather. Field areas prone to water stress are shown to lag as much as 23-33% below the field average during drought years and 26-33% during deluge years. By combining large-scale spatial datasets, we identify 2.65 million hectares of water-stress prone cropland, and estimate an aggregated economic loss impact of $536M USD yr -1 , 4.0 million tons yr -1 of less CO 2 fixed in crop biomass, and 52.6 Gg yr -1 of more reactive N in the environment. Yield stability maps can be used to spatially implement adaptation practices to mitigate weather-induced stresses in the most vulnerable cropland.

Climate change is likely to increase the frequency of drought and more extreme precipitation events. The objectives of this study were i) to assess the impact of extended drought followed by heavy precipitation events on yield and soil organic carbon (SOC) under historical and future climate, and ii) to evaluate the effectiveness of climate adaptation strategies (no-tillage and new cultivars) in mitigating impacts of increased frequencies of extreme events and warming. We used the validated SALUS crop model to simulate long-term maize and wheat yield and SOC changes of maize-soybean-wheat rotation cropping systems in the northern Midwest USA under conventional tillage and no-till for three climate change scenarios (one historical and two projected climates under the Representative Concentration Path (RCP) 4.5 and RCP6) and two precipitation changes (extreme precipitation occurring early or late season). Extended drought events caused additional yield reduction when they occurred later in the season (10-22% for maize and 5-13% for wheat) rather than in early season (5-17% for maize and 2-18% for wheat). We found maize grain yield declined under the projected climates, whereas wheat grain yield increased. No-tillage is able to reduce yield loss compared to conventional tillage and increased SOC levels (1.4-2.0 t/ha under the three climates), but could not reverse the adverse impact of climate change, unless early and new improved maize cultivars are introduced to increase yield and SOC under climate change. This study demonstrated the need to consider extreme weather events, particularly drought and extreme precipitation events, in climate impact assessment on crop yield and adaptation through no-tillage and new genetics reduces yield losses., Competing Interests: NO Authors have competing interests.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., (© 2018 John Wiley & Sons Ltd.)

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations., (© 2018 John Wiley & Sons Ltd.)

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer's field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term.

Estimates of climate change impacts on global food production are generally based on statistical or process-based models. Process-based models can provide robust predictions of agricultural yield responses to changing climate and management. However, applications of these models often suffer from bias due to the common practice of re-initializing soil conditions to the same state for each year of the forecast period. If simulations neglect to include year-to-year changes in initial soil conditions and water content related to agronomic management, adaptation and mitigation strategies designed to maintain stable yields under climate change cannot be properly evaluated. We apply a process-based crop system model that avoids re-initialization bias to demonstrate the importance of simulating both year-to-year and cumulative changes in pre-season soil carbon, nutrient, and water availability. Results are contrasted with simulations using annual re-initialization, and differences are striking. We then demonstrate the potential for the most likely adaptation strategy to offset climate change impacts on yields using continuous simulations through the end of the 21st century. Simulations that annually re-initialize pre-season soil carbon and water contents introduce an inappropriate yield bias that obscures the potential for agricultural management to ameliorate the deleterious effects of rising temperatures and greater rainfall variability.

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information., (© 2014 John Wiley & Sons Ltd.)

Warming generally leads to increased evaporative demand, altering the amount of water needed for growing crops. For the Midwest, some studies have suggested that reaching yield targets by 2050 will not be possible without additional precipitation or large expansion of irrigation. Here, we show that this claim is not supported by the historical summer climate trends, which indicate that the warming of daily average temperatures is largely driven by increases in minimum temperatures, while maximum temperatures have decreased. This has translated into a net decrease in vapor pressure deficit (VPD) and potential evapotranspiration (PET). With the increasing rainfall, this suggests that crop water deficits have likely become less frequent in the region despite the warming climate. By projecting these trends into 2050 and ancillary use of a crop model, we estimate minor changes in PET that would have minimal effects on corn yields (<6%) under persistence of these trends. Warming in the US Midwest is believed to increase the water needed to grow crops. This study finds that, on the contrary, due to rising rainfall and minimum temperature, and decreasing maximum temperature, potential crop water demand remains unchanged despite the warming climate. [ABSTRACT FROM AUTHOR]

The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass (Panicum virgatum) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha−1 year−1), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha−1 year−1). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha−1 year−1 under projected climate. On average, this was 0.3 Mg C ha−1 year−1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR. [ABSTRACT FROM AUTHOR]

Abstract: Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed. [ABSTRACT FROM AUTHOR]

Crop production in the Central High Plains is at an all-time high due to increased demand for biofuels, food, and animal products. Despite the need to produce more food by mid-century to meet expected population growth, under current management and genetics, crop production is likely to plateau or decline in the Central High Plains due to groundwater withdrawal at rates that greatly exceed recharge to the aquifer. The Central High Plains has experienced a consistent decline in groundwater storage due to groundwater withdrawal for irrigation greatly exceeding natural recharge. In this heavily irrigated region, water is essential to maintain yields and economic stability. Here, we evaluate how current trends in irrigation demand may impact groundwater depletion and quantify the impacts of these changes on crop yield and production through to 2099 using the well-established System Approach to Land Use Sustainability (SALUS) crop model. The results show that status quo groundwater management will likely reduce irrigated corn acreage by ~60% and wheat acreage by ~50%. This widespread forced shift to dryland farming, coupled with the likely effects of climate change, will contribute to overall changes in crop production. Taking into account both changes in yield and available irrigated acreage, corn production would decrease by approximately 60%, while production of wheat would remain fairly steady with a slight increase of about 2%. [ABSTRACT FROM AUTHOR]

Switchgrass ( Panicum virgatum) productivity on marginal and fertile lands has not been thoroughly evaluated in a systematic manner that includes soil-crop-weather-management interactions and to quantify the risk of failure or success in growing the crop. We used the Systems Approach to Land Use Sustainability ( SALUS) model to identify areas with low risk of failing to having more than 8000 kg ha−1 yr−1 switchgrass aboveground net primary productivity ( ANPP) under rainfed and unfertilized conditions. In addition, we diagnosed constraining factors for switchgrass growth, and tested the effect of nitrogen fertilizer application on plant productivity across Michigan for 30 years under three climate scenarios (baseline climate in 1981-2010, future climate with emissions using RCP 2.6 and RCP 6.0). We determined that <16% of land in Michigan may have at least 8 Mg ha−1 yr−1 ANPP under rainfed and unfertilized management with a low risk of failure. Of the productive low-risk land, about 25% was marginal land, with more than 80% of which was affected by limited water availability due to low soil water-holding capacity and shallow depth. About 80% of the marginal land was N limited under baseline conditions, but that percentage decreased to 58.5% and 42.1% under RCP 2.6 and RCP 6.0 climate scenarios, respectively, partly due to shorter growing season, smaller plants and less N demand. We also found that the majority of Michigan's land could have high switchgrass ANPP and low risk of failure with no more than 60 kgN ha−1 fertilizer input. We believe that the methodology used in this study works at different spatial scales, as well as for other biofuel crops. [ABSTRACT FROM AUTHOR]

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha) of the distribution. [ABSTRACT FROM AUTHOR]

The response of wheat crops to elevated CO2 ( eCO2) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha−1 and 1600 to 3900 kg ha−1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol−1 CO2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm ( P = 0.10) by eCO2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY- CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency ( RUE) or transpiration efficiency ( TE) was not critical to define the overall response to eCO2. However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2, water and temperature is required to resolve these model discrepancies. [ABSTRACT FROM AUTHOR]

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models' historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments. [ABSTRACT FROM AUTHOR]

Abstract: Olive is of major eco-social importance for the Mediterranean Basin, a climate change and biodiversity hotspot of global relevance where remarkable climate change is expected over the next few decades with unknown ecosystem impacts. However, climate impact assessments on terrestrial ecosystems have long been constrained by a narrow methodological basis (ecological niche models, ENMs) that is correlative and hence largely omits key impact drivers such as trophic interactions and the effect of water availability, the latter being especially relevant to desertification- prone Mediterranean ecosystems. ENMs use correlative measures of water availability unsuitable for making projections about the future. To bridge this gap, mechanistic approaches such as physiologically-based weather- driven demographic models (PBDMs) may be used as they embed by design both the biology of trophic interactions and a mechanistic representation of soil water balance. Here we report progress towards assessing climate effects on olive culture across the Mediterranean region using mechanistic PBDMs that project regionally the multitrophic population dynamics of olive and olive fly as affected by daily weather and soil water balance. [Copyright &y& Elsevier]

There is an urgent need to transform unsustainable "linear" grain production systems in the United States (U.S.) and other countries like China, Brazil, Argentina, Canada, Russia, Australia and Europe, into more circular and sustainable systems to address the simultaneous challenges of resource depletion, environmental degradation, and the growing global demand for food under the threat of climate change. In this perspective, we survey the current state of circularity of U.S. grain production, and discuss how we can transform the systems into more circular systems. Specifically, we lay out a vision of circular grain production enabled by novel digital, mechanical, and biological technologies that allow closing loops of nutrient and energy flows within the farm, through the optimization of land-use choices and crop management. We also examine market- and policy-based mechanisms that could incentivize the widespread adoption of these key technologies. [Display omitted] • There is an urgent need to transform "linear" grain production systems into circular and sustainable systems • Digital agriculture technologies can increase circularity in the grain production systems • Regenerative practices, new genetics, robotics and electrification of fertilizer production will increase circularity • Widespread adoption of circular systems will depend critically on their profitability • Profitability will in turn depend on public policies needed to encourage farmers to adopt sustainable practices [ABSTRACT FROM AUTHOR]

Abstract: The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies. [Copyright &y& Elsevier]

Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to an article published in the previous issue by D. B. Lobell and colleagues about the effects of temperature and rainfall on maize yield in the U.S.

Simulation models, informed and validated with datasets from long term experiments (LTEs), are considered useful tools to explore the effects of different management strategies on soil organic carbon (SOC) dynamics and evaluate suitable mitigative options for climate change. But, while there are several studies which assessed a better prediction of crop yields using an ensemble of models, no studies are currently available on the evaluation of a model ensemble on SOC stocks. In this study we assessed the advantages of using an ensemble of crop models (APSIM-NWheat, DSSAT, EPIC, SALUS), calibrated and validated with datasets from LTEs, to estimate SOC dynamics. Then we used the mean of the model ensemble to assess the impacts of climate change on SOC stocks under conventional (CT) and conservation tillage practices (NT: No Till; RT: Reduced Tillage). The assessment was completed for two long-term experiment sites (Agugliano – AN and Pisa – PI2 sites) in Italy under rainfed conditions. A durum wheat ( Triticum turgidum subsp. durum (Desf.) Husn.) – maize ( Zea mays L.) rotation system was evaluated under two different climate scenarios over the periods 1971–2000 (CP: Present Climate) and 2021–2050 (CF: Future Climate), generated by setting up a statistical model based on canonical correlation analysis. Our study showed a decrease of SOC stocks in both sites and tillage systems over CF when compared with CP. At the AN site, CT lost −7.3% and NT −7.9% of SOC stock (0–40 cm) under CF. At the PI2 site, CT lost −4.4% and RT −5.3% of SOC stocks (0–40 cm). Even if conservation tillage systems were more impacted under future scenarios, they were still able to store more SOC than CT, so that these practices can be considered viable options to mitigate climate change. Furthermore, at the AN site, under CF, NT demonstrated an annual increase of 0.4%, the target value suggested by the 4 per thousand initiative launched at the 21st meeting of the Conference of the Parties in Paris. However, RT at the PI2 needs to be coupled with other management strategies, as the introduction of cover crops, to achieve such target. [ABSTRACT FROM AUTHOR]

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. [ABSTRACT FROM AUTHOR]

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R 2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. [ABSTRACT FROM AUTHOR]

Abstract: Soil organic carbon sequestration rates over 20 years based on the Intergovernmental Panel for Climate Change (IPCC) methodology were combined with local economic data to determine the potential for soil C sequestration in wheat-based production systems on the Indo-Gangetic Plain (IGP). The C sequestration potential of rice–wheat systems of India on conversion to no-tillage is estimated to be 44.1MtC over 20 years. Implementing no-tillage practices in maize–wheat and cotton–wheat production systems would yield an additional 6.6MtC. This offset is equivalent to 9.6% of India''s annual greenhouse gas emissions (519MtC) from all sectors (excluding land use change and forestry), or less than one percent per annum. The economic analysis was summarized as carbon supply curves expressing the total additional C accumulated over 20 year for a price per tonne of carbon sequestered ranging from zero to USD 200. At a carbon price of USD 25MgC−1, 3MtC (7% of the soil C sequestration potential) could be sequestered over 20 years through the implementation of no-till cropping practices in rice–wheat systems of the Indian States of the IGP, increasing to 7.3MtC (17% of the soil C sequestration potential) at USD 50MgC−1. Maximum levels of sequestration could be attained with carbon prices approaching USD 200MgC−1 for the States of Bihar and Punjab. At this carbon price, a total of 34.7MtC (79% of the estimated C sequestration potential) could be sequestered over 20 years across the rice–wheat region of India, with Uttar Pradesh contributing 13.9MtC. [Copyright &y& Elsevier]