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Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework.

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO concentrations (e[CO ]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programs might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO ] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO ] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO ] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour.

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Tropospheric ozone threatens crop production in many parts of the world, especially in highly populated countries in economic transition. Crop models suggest substantial global yield losses for wheat, but typically such models fail to address differences in ozone responses between tolerant and sensitive genotypes. Therefore, the purpose of this study was to identify physiological traits contributing to yield losses or yield stability under ozone stress in 18 contrasting wheat cultivars that had been pre-selected from a larger wheat population with known ozone tolerance. Plants were exposed to season-long ozone fumigation in open-top chambers at an average ozone concentration of 70 ppb with three additional acute ozone episodes of around 150 ppb. Compared to control conditions, average yield loss was 18.7 percent, but large genotypic variation was observed ranging from 2.7 to 44.6 percent. Foliar chlorophyll content represented by normalized difference vegetation index and net CO

Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability (p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha-1, with a maximum of 31207 kg ha-1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.

Global food security requires food production to be increased in the coming decades. The closure of any existing genetic yield gap (Yig) by genetic improvement could increase crop yield potential and global production. Here we estimated present global wheat Yig, covering all wheat-growing environments and major producers, by optimizing local wheat cultivars using the wheat model Sirius. The estimated mean global Yig was 51%, implying that global wheat production could benefit greatly from exploiting the untapped global Yig through the use of optimal cultivar designs, utilization of the vast variation available in wheat genetic resources, application of modern advanced breeding tools, and continuous improvements of crop and soil management.

Most large scale studies assessing climate change impacts on crops are performed with simulations of single crops and with annual re-initialization of the initial soil conditions. This is in contrast to the reality that crops are grown in rotations, often with sizable proportion of the preceding crop residue to be left in the fields and varying soil initial conditions from year to year. In this study, the sensitivity of climate change impacts on crop yield and soil organic carbon to assumptions about annual model re-initialization, specification of crop rotations and the amount of residue retained in fields was assessed for seven main crops across Europe. Simulations were con-ducted for a scenario period 2040-2065 relative to a baseline from 1980 to 2005 using the SIMPLACE1 modeling framework. Results indicated across Europe positive climate change impacts on yield for C3 crops and negative impacts for maize. The consideration of simulating rotations did not have a benefit on yield variability but on relative yield change in response to climate change which slightly increased for C3 crops and decreased for C4 crops when rotation was considered. Soil organic carbon decreased under climate change in both simulations assuming a continuous monocrop and plausible rotations by between 1% and 2% depending on the residue management strategy.

With the occurrence of extreme events projected to increase under climate change, it is critical to assess the risk they pose to food security and identify suitable adaptation options. While mechanisms and impacts of climatic stressors (e.g. frost, drought, heat or flooding) have been studied individually, little is known their combined impacts on crops to be expected under actual production conditions. This lack of process knowledge is reflected in the few instances of crop models considering multiple stressors. Here we provide an overview of the representation of single stressors in process based crop models. From this basis, a framework to consider multiple stressors in current models is presented, defining four stressor combination types: 1. Single exposure; 2. No direct interaction; 3. Known interaction; and 4. Unknown interaction. An analytical framework from ecological sciences is then presented as an approach to consider when formulating algorithms for the 4th type of unknown interactions. In a final section, we discuss new data driven and model based exploration options to support understanding multiple stressor interactions in recognition of the challenges of experimentation around multiple stressors. We assert that process based modeling has a large and largely untapped potential to support scientific investigations of the underlying mechanisms driving crop response to multiple stressors.

The war in Ukraine threatened to block 9% of global wheat exports, driving wheat prices to unprecedented heights. We advocate, that in the short term, compensating for such an export shortage will require a coordinated release of wheat stocks, while if the export block persists, other export countries will need to fill the gap by increasing wheat yields or by expanding wheat cropping areas by 8% in aggregate. We estimate that a production increase would require an extra half a million tons of nitrogen fertilizer, yet fertilizer prices are at record levels, driven by rising energy prices. Year-to-year variability plus more frequent climate change-induced crop failures could additionally reduce exports by another 5 to 7 million tons in any given year, further stressing global markets. Without stabilizing wheat supplies through judicious management of stocks and continuing yield improvements, food and national security are at risk across many nations in the world.

Wheat production in Brazil is insufficient to meet domestic demand and falls drastically in response to adverse climate events. Multiple, agro-climate-specific regression models, quantifying regional production variability, were combined to estimate national production based on past climate, cropping area, trend-corrected yield, and national commodity prices. Projections with five CMIP6 climate change models suggest extremes of low wheat production historically occurring once every 20 years would become up to 90% frequent by the end of this century, depending on representative concentration pathway, magnified by wheat and in some cases by maize price fluctuations. Similar impacts can be expected for other crops and in other countries. This drastic increase in frequency in extreme low crop production with climate change will threaten Brazil's and many other countries progress toward food security and abolishing hunger. ISSN:1748-9326 ISSN:1748-9318

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

International audience; Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.

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 CO2 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 CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 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., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).

The impact of increasing rates of typically used mineral fertilizer on Rain water use efficiency (WUE) and Radiation use efficiency (RUE) of maize grain yield and stover biomass productivity was estimated across the Agro-Ecological Zones (AEZs) of Ethiopia using the crop model LINTUL5 embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modeling Platform for Advanced Crop and Ecosystem Management) with the hypothesis that WUE and RUE would increase with higher application rates of mineral fertilizer and vary for maize grain yield and stover biomass across the AEZs. The simulations were run using a long maturing cycle maize variety (BH660) and a medium maturing cycle maize variety (BH540) with historical weather data (2004–2010).There were strong effects of the application rate of mineral fertilizer on WUE and RUE of maize yield and stover biomass across the AEZs. The highest WUE of 11.5 kg mm−1 and 9.4 kg mm−1 in maize grain yield and stover biomass respectively was estimated with the application of 315 kg N ha−1 + 105 kg P ha−1 in AEZ 3 having the lowest amount of rainfall during the crop growth period as compared with AEZ 1 and 2.The findings of the current study indicate that WUE in grain and stover production can be increased to by 172% to 363%, and 230% to 352% respectively depending upon the AEZs, based on management intervention in terms of increased fertilizer application rates as compared with the WUE under unfertilized conditions. On the other hand, the highest RUE of 3.0 kg MJ−1 and 2.1 kg MJ−1 in maize grain yield and stover biomass respectively was estimated in AEZ 2 with the application of 315 kg N ha−1 + 105 kg P ha−1. RUE in grain yield and stover biomass can be increased to the tune of 177% to 362%, and 216% to 351% respectively depending upon the AEZs with the increased application of N and P compared with the RUE under unfertilized conditions. The economic analysis indicates optimal fertilizer application levels of 225 N + 75P kg ha−1 for maize production under average national conditions and prices and a slightly lower rate of 180 N + 60P kg ha−1 in regions where water availability tends to constrain grain yields in addition to the nutrient deficit.

Drought is one of the most detrimental factors limiting crop growth and production of important staple crops such as winter wheat and maize. For both crops, stomatal regulation and change of canopy structure responses to water stress can be observed. A substantial range of stomatal behavior in regulating water loss was recently reported while the crop growth and morphological responses to drought stress depend on the intensity and duration of the imposed stress. Insights into the responses from leaf to the canopy are important for crop modeling and soil-vegetation-atmosphere models (SVAT). Stomatal responses and effects of soil water deficit on the dynamic change of canopy photosynthesis and transpiration, and seasonal crop growth of winter wheat and maize are investigated based on data collected from field-grown conditions with varying soil moisture treatments (sheltered, rainfed, irrigated) in 2016, 2017, and 2018. A reduction of leaf net photosynthesis (An), stomatal conductance (Gs), transpiration (E), and leaf water potential (LWP) was observed in the sheltered plot as compared to the rainfed and irrigated plots in winter wheat in 2016, indicating anisohydric stomatal responses. Maize showed seasonal isohydric behaviour with the minimum LWP from -1.5 to -2 MPa in 2017 and -2 to -2.7 MPa in the extremely hot and dry year in 2018. Crop growth (biomass, leaf area index, and yield) was substantially reduced under drought conditions, particularly for maize in 2018. Leaf water use efficiency (An/E) and crop WUE (total dry biomass/canopy transpiration) were not significantly different among treatments in both crops. The reduction of tiller number (in winter wheat) and leaf-rolling and plant size (in maize) resulted in a reduction of canopy transpiration, assimilation rate, and thus biomass. The seasonal isohydry in maize and the seasonal variability of LWP in winter wheat suggest a possibility to use the same critical LWP thresholds for maize and wheat to simulate the stomatal control in process-based crop and SVAT models. The canopy response such as dynamically reducing leaf area under water stress adds complexity in simulating gas exchange and crop growth rate that needs adequate consideration in the current modeling approaches.

In recent years, evidence of recent climate change has been identified in South America, affecting agricultural production negatively. In response to this, our study employs a crop modelling approach to estimate the effects of recent climate change on maize yield in four provinces of Ecuador. One of them belongs to a semi-arid area. The trend analysis of maximum temperature, minimum temperature, precipitation, wind speed, and solar radiation was done for 36 years (from 1984 to 2019) using the Mann–Kendall test. Furthermore, we simulated (using the LINTUL5 model) the counterfactual maize yield under current crop management in the same time-span. During the crop growing period, results show an increasing trend in the temperature in all the four studied provinces. Los Rios and Manabi showed a decreasing trend in radiation, whereas the semi-arid Loja depicted a decreasing precipitation trend. Regarding the effects of climate change on maize yield, the semi-arid province Loja showed a more significant negative impact, followed by Manabi. The yield losses were roughly&nbsp, 40kg ha−1 and 10 kg ha−1 per year, respectively, when 250 kg N ha−1 is applied. The simulation results showed no effect in Guayas and Los Rios. The length of the crop growing period was significantly different in the period before and after 2002 in all provinces. In conclusion, the recent climate change impact on maize yield differs spatially and is more significant in the semi-arid regions.

In low input agriculture, a thorough understanding of the plant-nutrient interactions plays a central role. This study aims to investigate the effects of nitrogen (N), phosphorus (P), and potassium (K) and liming omission on shoot growth as well as on topsoil root biomass, growth and morphology (tuber and fibrous roots) of sugar beet grown under field conditions at the Dikopshof long-term fertilizer experiment (Germany). Classical shoot observation methods were combined with root morphology and link measurements using an image analysis program. Omission of the nutrients N, P and K as well as of liming led to a significant decrease in shoot growth. Tuber yield was lowest for the unfertilized and the K omission treatment. The root shoot ratio was highest in the N deficient treatment. In the K omission treatment, a strategic change from a less herringbone root type (early stage) to a more herringbone root type (late stage), which is more efficient for the acquisition of mobile nutrients, was observed. By contrast, a change from a more herringbone (early stage) to a less herringbone root type (late stage) which is less expensive to produce and maintain was observed in the unfertilized treatment. We conclude that sugar beet alters its root morphology as a nutrient acquisition strategy.

Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important nutrients. We analysed the stability of four major European crops grown between 1955 and 2008 at a long-term fertilization experiment located in Germany. Six fertilizer treatments ranged from no fertilization over the omission of individual macronutrients to complete mineral fertilization with all major macronutrients (nitrogen, phosphorus, potassium and calcium). Yield stability was estimated for each crop × treatment combination using the relative yield deviation in each year from the corresponding (nonlinear) trend value (relative yield anomalies (RYA)). Stability was lowest for potato, followed by sugar beet and winter wheat and highest for winter rye. Stability was highest when soils had received all nutrients with the standard deviation of RYA being two to three times lower than for unfertilized plots. The omission of nitrogen and potassium was associated with a decrease in yield stability and a decrease in the number of simultaneous positive and negative yield anomalies among treatments. Especially in root crops nutrient supply strongly influenced both annual yield anomalies and changes in anomalies over time. During the second half of the observation period yield stability decreased for sugar beet and increased for winter wheat. Potato yields were more stable during the second period, but only under complete nutrient supply. The critical role of potassium supply for yield stability suggests potential links to changes in the water balance during the last decades. Results demonstrate the need to explicitly consider the response of crops to long-term nutrient supply for understanding and predicting changes in yield stability.

Winter cover crops are sown in between main spring crops (e.g. cash and forage crops) to provide a range of benefits, including the reduction of nitrogen (N) leaching losses to groundwater. However, the extent by which winter cover crops will remain effective under future climate change is unclear. We assess variability and uncertainty of climate change effects on the reduction of N leaching by winter oat cover crops. Field data were collected to quantify ranges of cover crop above-ground biomass (7 to 10 t DM/ha) and N uptake (70 to 180 kg N/ha) under contrasting initial soil conditions. The data were also used to evaluate the APSIM-NextGen model (R2 from 62 to 96% and RMSEr from 7 to 50%), which was then applied to simulate cover crop and fallow conditions across four key agricultural locations in New Zealand, under baseline and future climate scenarios. Cover crops reduced N leaching risks for all location/scenario combinations but with large variability in space and time (e.g. 21 to 47% of fallow) depending on the climate change scenario. For instance, end-of-century estimates for northern (warmer) locations mostly showed non-significant effects of climate change on cover crop effectiveness and N leaching. In contrast for southern (colder) locations, there was a systematic increase in N leaching risks with climate change intensity despite a concomitant, but less than proportional, increase in cover crop effectiveness (up to ~5% of baseline) due to higher winter yields and N uptake. This implies that climate change may not only modify the geography of N leaching hotspots, but also the extent by which cover crops can locally reduce pollution risks, in some cases requiring complementary adaptive measures. The patchy- and threshold-nature of leaching events indicates that fine spatio-temporal resolutions are better suited to evaluate cover crop effectiveness under climate change.

Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate.

International audience; Robust projections of climate impact on crop growth and productivity by crop models are key to designing effective adaptations to cope with future climate risk. However, current crop models diverge strongly in their climate impact projections. Previous studies tried to compare or improve crop models regarding the impact of one single climate variable. However, this approach is insufficient, considering that crop growth and yield are affected by the interactive impacts of multiple climate change factors and multiple interrelated biophysical processes. Here, a new comprehensive analysis was conducted to look holistically at the reasons why crop models diverge substantially in climate impact projections and to investigate which biophysical processes and knowledge gaps are key factors affecting this uncertainty and should be given the highest priorities for improvement. First, eight barley models and eight climate projections for the 2050s were applied to investigate the uncertainty from crop model structure in climate impact projections for barley growth and yield at two sites: Jokioinen, Finland (Boreal) and Lleida, Spain (Mediterranean). Sensitivity analyses were then conducted on the responses of major crop processes to major climatic variables including temperature, precipitation, irradiation, and CO2, as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO2 relationships in the models were the major sources of the large discrepancies among the models in climate impact projections. In particular, the impacts of increases in temperature and CO2 on leaf area development were identified as the major causes for the large uncertainty in simulating changes in evapotranspiration, above-ground biomass, and grain yield. Our findings highlight that advancements in understanding the basic processes and thresholds by which climate warming and CO2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts.

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO

Achieving food and nutrition security for all in a changing and globalized world remains a critical challenge of utmost importance. The development of solutions benefits from insights derived from modelling and simulating the complex interactions of the agri-food system, which range from global to household scales and transcend disciplinary boundaries. A wide range of models based on various methodologies (from food trade equilibrium to agent-based) seek to integrate direct and indirect drivers of change in land use, environment and socio-economic conditions at different scales. However, modelling such interaction poses fundamental challenges, especially for representing non-linear dynamics and adaptive behaviours.We identify key pieces of the fragmented landscape of food security modelling, and organize achievements and gaps into different contextual domains of food security (production, trade, and consumption) at different spatial scales. Building on in-depth reflection on three core issues of food security – volatility, technology, and transformation – we identify methodological challenges and promising strategies for advancement.We emphasize particular requirements related to the multifaceted and multiscale nature of food security. They include the explicit representation of transient dynamics to allow for path dependency and irreversible consequences, and of household heterogeneity to incorporate inequality issues. To illustrate ways forward we provide good practice examples using meta-modelling techniques, non-equilibrium approaches and behavioural-based modelling endeavours. We argue that further integration of different model types is required to better account for both multi-level agency and cross-scale feedbacks within the food system.

Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. Here, we review the use of ‘crop diversification’ measures in agricultural research. We (i) analyse changes in crop diversification studies over time; (ii) identify diversification practices based on empirical studies; (iii) differentiate their use by country, crop species and experimental setup and (iv) identify target parameters to assess the success of diversification. Our main findings are that (1) less than 5% of the selected studies on crop diversification refer to our search term ‘diversification’; (2) more than half of the studies focused on rice, corn or wheat; (3) 76% of the experiments were conducted in India, USA, Canada, Brazil or China; (4) almost any arable crop was tested on its suitability for diversification; (5) in 72% of the studies on crop diversification, at least one additional agronomic measure was tested and (6) only 45% of the studies analysed agronomic, economic and ecological target variables. Our findings show the high variability of approaches to crop diversification and the lack of a consistent theoretical concept. For better comparability and ability to generalise the results of the different primary studies, we suggest a novel conceptual framework. It consists of five elements, (i) definition of the problem of existing farming practices and the potential need for diversification, (ii) characterisation of the baseline system to be diversified, (iii) definition of the scale and target area, (iv) description of the experimental design and target variables and (v) definition of the expected impacts. Applying this framework will contribute to utilizing the benefits of crop diversification more efficiently.

This chapter describes the structure, datasets and processing methods of a new spatial analysis framework to assess the response of agricultural landscapes to climates and soils. Georeferenced gridded information on climate (historical and climate change scenarios), soils, terrain and crop management are dynamically integrated by a process-based biophysical model within a high-performance computing environment. The framework is used as a research tool to quantify productivity and environmental aspects of agricultural systems. An application case study using New Zealand spatial datasets and silage maize cropping systems illustrates the current framework capability and highlights key areas for enhancement in future gridded modelling research.

Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models.

The simulated data set described in this paper was created by an ensemble of nine different crop models: HERMES (HE), Simplace (L5), SiriusQuality (SQ), MONICA (MO), Sirius2014 (S2), FASSET (FA), 4M (4M), SSM (SS), DSSAT-CSM IXIM (IX). Simulations were performed for grain maize (six models) and winter wheat (eight models) under diverse conditions over agriculturally relevant areas in the EU-27 at a 25 x 25 km spatial resolution. Simulations were drawn from combinations of three representative concentration pathways and climate outputs from five general circulation models for time periods 2040-2069 and 2070-2099. Historical climate data was the basis for simulation years 1980-2010 and considered as a baseline. Simulation results from 1980-2010 and 2040-2069 were used to analyze crop responses to changing climatic variables and their diverging sensitivities to these variables. This data paper describes the creation, motivation and format of the simulation results to enable others to use the data set.

Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.

Agriculture is a critical element of the West African Sudan Savanna’s economy and the main livelihood strategy for many people, where food insecurity and poverty are widespread. Low soil fertility, high intra and inter-annual rainfall variability together with limited ability to invest in new technologies are among the major constraints to the predominantly smallholder, rainfed crop production in the region’s mixed crop-livestock systems. We evaluated the effect of tillage practices (contour and reduced tillage), nitrogen fertilizer rates (no nitrogen -N0, recommended nitrogen -NREC and high nitrogen-N2REC) and residue management (improved and standard) on the yield of maize, cotton and sorghum for two landscape positions (upslope and footslope) for four growing seasons in three locations in the Sudan Savanna region of Burkina-Faso (Dano), Ghana (Vea) and Republic of Benin (Dassari). The studies aimed at assessing the potential of residue retention, tillage practices and nitrogen fertilization to (1) increase average yields and (2) stabilize yields under sub-humid conditions. Over the 4 year study period, across the 3 locations recommended N produced 17% higher crop yields than the N0 treatment. It was not worthwhile to double the recommended N as there was no yield benefit of applying more fertilizer. The results revealed no consistent interactions across sites except crop type and nitrogen fertilization. Stability analysis revealed that contour ridging was superior in stabilizing yield. Evaluation of the relative maize yield stability on different levels of nitrogen depicts variation and there is a tendency for an inverse relationship between mean yield of nitrogen levels and its relative yield stability since nitrogen application did not increase yield stability. The outcome of cost benefit analysis revealed that, return per cash invested favored non-degraded sites with cotton production and in years with favorable rainfall conditions. Thus, in the Sudan savanna of West Africa, it is economically risky to invest in mineral N fertilizers when cropping on degraded soils or when rainfall is expected to be erratic.

Although the root length density (RLD) of crops depends on their root system architecture (RSA), the root growth modules of many 1D field crop models often ignored the RSA in the simulation of the RLD. In this study, two model set‐up scenarios were used to simulate the RLD, above‐ground biomass (AGB) and grain yield (GY) of water‐stressed spring wheat in Germany, aiming to investigate the impact of improved RLD on AGB and GY predictions. In scenario 1, SlimRoot, a root growth sub‐model that does not consider the RSA of the crop, was coupled to a Lintul5‐SlimNitrogen‐SoilCN‐Hillflow1D crop model combination. In scenario 2, SlimRoot was replaced with the Somma sub‐model which considered the RSA for simulating RLD. The simulated RLD, AGB and GY were compared with observations. Scenario 2 predicted the RLD, AGB and GY with an average root mean square error (RMSE) of 0.43 cm/cm³, 0.59 t/ha and 1.03 t/ha, respectively, against 1.03 cm/cm³, 1.20 t/ha and 2.64 t/ha for scenario 1. The lower RMSE under scenario 2 shows that, even under water‐stress conditions, predictions of GY and AGB can be improved by considering the RSA of the crop for simulating the RLD.

Tillage is a primary field operation aiming to modify the soil structure to favour agronomic and soil related processes such as soil seed contact, root proliferation, water infiltration, incorporation of residues, break down of soil organic matter and land forming. The modification of the soil physical and chemical properties especially in the upper soil layers after a tillage operation can be huge. The application of field-scale crop growth models is a widely accepted tool for process understanding but also to support an efficient and sustainable crop production. Agro-ecosystem models are composed of different sub-modules for certain processes related to crop growth and soil-nutrient and water dynamics in response to atmospheric conditions. In this study, the approaches to simulate the impact of tillage on soil physical properties and on vertical distribution of organic matter and nutrients implemented in 16 different agro-ecosystem models (APEX, APSIM, CropSyst, DAISY, DayCent, DNDC, DSSAT, EPIC, HERMES, HYDRUS-1D, LPJmL, MONICA, SALUS, SPACSYS, STICS, and SWAT) are reviewed. Some of the reviewed agro-ecosystem models simulate the tillage effects on soil bulk density, soil settlement, soil texture redistribution, and several soil hydraulic properties. To some extent, the changes in soil porosity, soil aggregates, and the soil organic matter content are considered. Most models simulate the incorporation or/and redistribution of organic matter, residues or/and nutrients in the soil. None of the models consider the changes in biochemical properties such as changes in soil microbial biomass and activity or redistribution of weed seeds after a tillage operation. This study indicates the urgent need to improve the tillage components in crop modelling due to its obvious impact on various soil and nutrient processes and consequently, on crop growth and yield.

Impacts of nutrient supply and different cultivars (genotypes) on actual yield levels have been studied before, but the long-term response of yield trends is hardly known. We present the effects of 24 different fertilizer treatments on long-term yield trends (1953–2009) of winter wheat, winter rye, sugar beet and potato, with improved cultivars changing gradually over time. Data was obtained from the crop rotation within the long-term fertilization experiment at Dikopshof, Germany. Yield trends were derived as the slope regression estimates between adjusted yield means and polynomials of the first year of cultivation of each tested cultivar, when tested for more than two years. A linear trend fitted best all data and crops. Yields in highly fertilized treatments increased linearly, exceeding 0.08 t ha−1 a−1 for both, winter wheat and winter rye, and ≥0.30 and ≥0.20 t ha−1 a−1 for sugar beet and potato fresh matter yields. Yield trends of winter cereals and sugar beet increased over time at N rates ≥40 kg ha−1 a−1, being 0.04–0.10 t ha−1 a−1 for cereals and 0.26–0.34 t ha−1 a−1 for sugar beet, although N rates >80 kg ha−1 a−1 produced a stronger effect. Nitrogen was the most influential nutrient for realisation of the genetic yield potential. Additional supply of P and K had an effect on yield trends for rye and sugar beet, when N fertilization was also sufficient; high K rates benefited potato yield trends. We highlight the importance of adequate nutrient supply for maintaining yield progress to actually achieve the crop genetic yield potentials. The explicit consideration of the interaction between crop fertilization and genetic progress on a long-term basis is critical for understanding past and projecting future yield trends. Long-term fertilization experiments provide a suitable data source for such studies.

The environmental and economic sustainability of future cropping systems depends on adaptation to climate change. Adaptation studies commonly rely on agricultural systems models to integrate multiple components of production systems such as crops, weather, soil and farmers' management decisions. Previous adaptation studies have mostly focused on isolated monocultures. However, in many agricultural regions worldwide, multi-crop rotations better represent local production systems. It is unclear how adaptation interventions influence crops grown in sequences. We develop a catchment-scale assessment to investigate the effects of tactical adaptations (choice of genotype and sowing date) on yield and underlying crop-soil factors of rotations. Based on locally surveyed data, a silage-maize followed by catch-crop-wheat rotation was simulated with the APSIM model for the RCP 8.5 emission scenario, two time periods (1985-2004 and 2080-2100) and six climate models across the Kaituna catchment in New Zealand. Results showed that direction and magnitude of climate change impacts, and the response to adaptation, varied spatially and were affected by rotation carryover effects due to agronomical (e.g. timing of sowing and harvesting) and soil (e.g. residual nitrogen, N) aspects. For example, by adapting maize to early-sowing dates under a warmer climate, there was an advance in catch crop establishment which enhanced residual soil N uptake. This dynamics, however, differed with local environment and choice of short- or long-cycle maize genotypes. Adaptation was insufficient to neutralize rotation yield losses in lowlands but consistently enhanced yield gains in highlands, where other constraints limited arable cropping. The positive responses to adaptation were mainly due to increases in solar radiation interception across the entire growth season. These results provide deeper insights on the dynamics of climate change impacts for crop rotation systems. Such knowledge can be used to develop improved regional impact assessments for situations where multi-crop rotations better represent predominant agricultural systems.

Changing crop phenology is considered an important bio-indicator of climate change, with the recent warming trend causing an advancement in crop phenology. Little is known about the contributions of changes in sowing dates and cultivars to long-term trends in crop phenology, particularly for winter crops such as winter wheat. Here, we analyze a long-term (1952–2013) dataset of phenological observations across western Germany and observations from a two-year field experiment to directly compare the phenologies of winter wheat cultivars released between 1950 and 2006. We found a 14–18% decline in the temperature sum required from emergence to flowering for the modern cultivars of winter wheat compared with the cultivars grown in the 1950s and 1960s. The trends in the flowering day obtained from a phenology model parameterized with the field observations showed that changes in the mean temperature and cultivar properties contributed similarly to the trends in the flowering day, whereas the effects of changes in the sowing day were negligible. We conclude that the single-cultivar concept commonly used in climate change impact assessments results in an overestimation of winter wheat sensitivity to increasing temperature, which suggests that studies on climate change effects should consider changes in cultivars.

Previous studies suggested a wide range of sensitivities of wheat yields to heat stress around anthesis. The aim of this study was to improve the understanding of the reasons of the disagreement by testing the response of wheat yield and yield components to differences in the method of heating, the temperature measurement point and soil substrate under sole heat and combined heat and drought stress around anthesis. Growth chamber experiments performed at different sites showed that increasing of the ambient air temperature at anthesis corresponding to a temperature sum of 12000 °C min above 31 °C resulted in a significant yield reduction of −24% for plants grown on sandy soil substrate but not for those grown on a soil with high soil water holding capacity. The grain yield of wheat also declined by −16% for sandy soil substrate but at a much lower level of heat stress when the temperature of the ears was increased by infrared heaters (a temperature sum of 1900 °C min above 31 °C). The yield reduction increased significantly under combined heat and drought compared to sole heat stress. Grain number significantly declined in all experiments with heat stress and combined heat and drought stress at anthesis. Single grain weight increased with heat stress around anthesis and partly compensated for lower grain numbers of pots containing a soil with high soil water holding capacity but not in experiments with sandy soil substrate. We demonstrate, based on data from previous heat stress studies, that statistical relationships between crop heat stress and yield loss become stronger when separating the data according to the soil used in the experiments. Our results suggest that the differences in the yield response to heat may be caused by additional drought stress which is difficult to avoid in heat stress experiments using sandy soil substrate. We conclude that differences in the experimental setup of heat stress experiments substantially influence the crop response to heat stress and need to be considered when using the data to calibrate crop models applied for climate change impact assessments.

Climate change is unequivocal and these changes have increased over the past few years. The recent vulnerability and prospect of climate variability and change impact, thus, warrants measures now to reduce the adverse impacts. This study presents an estimate of the effects of climate variables on potential maize productivity and an assessment of the most limiting climatic drivers in the future climate scenarios for maize production in central Ghana, constituting major maize production areas. The time-slices 2000, 2030 and 2080 were chosen to represent the baseline, near future and end century climate, respectively. Furthermore, two Representative Concentration Pathways (RCP s ) namely RCP 4.5 and RCP 8.5 from the GFDL-ESM2M, GISS-E2-H, and HadGEM2-ES, General Circulation Models (GCMs), were selected. Simulations based on the model LINTUL5 were used to estimate the crop responses. There is an average increase in the maize yield and aboveground biomass in the projected scenarios by 57% and 59% respectively under HadGEM2-ES (RCP 8.5) in the time horizon 2030. However, variability in the projected average maize yield and above ground biomass compared to the baseline values, is ranging from 183.6 kg ha − 1 under HadGEM2-ES (RCP 8.5) by time horizon 2080 to a maximum of 1326.8 kg ha − 1 under HadGEM2-ES (RCP 8.5) by 2030 and a minimum increase of 169.9 kg ha − 1 under GFDL-ESM2M (RCP 8.5) by time horizon 2080 to a maximum increase of 2386.1 kg ha − 1 under HadGEM2-ES (RCP 8.5) by time horizon 2030. The reasons for potential benefit in maize yields across the climate scenarios was attributed to the positive effect of CO 2 , reduced water stress reflected by lower atmospheric water demand during crop growth period. It also indicates that water is the limiting factor for maize production in the study region. However, temperature (through shortening of the maize growing cycle), and solar radiation may remain the limiting factors for maize production.

Phosphorus (P) is an essential plant nutrient. However, our understanding of the complex interactions between soil P availability, environment, management and crop growth is still limited. We used unique historic and recent soil and crop data spanning more than a century combined with a process-based crop model to analyze the impact of P fertilizer omission and P fertilization on the biomass production of five crops. The long-term field experiment at Dikopshof, Germany, was established in 1904 with a 5-year crop rotation of sugar beet, winter wheat, winter rye, clover and oat/potato (potato replaced oat in 1953) on a fertile Luvisol. Averaged over the period from 1906 to 2018, the yield loss due to P omission was low for winter wheat and winter rye (7-8 %). In contrast, yield losses for sugar beet, clover and potato were relatively high (15-24 %). The yield loss from P fertilizer omission in comparison to the reference treatment (rotation mean excluding oat/potato) increased until the middle of the last century from 7% to 18 %, but subsequently decreased to 13 %. Trend and correlation analyses suggest that this decrease was related to an increase in air temperatures in especial during spring and a lower yield loss under P omission. Crop model simulations showed decreasing topsoil organic carbon concentrations after the 1930ies as manure was discontinued in 1942 but also due to increasing air temperatures. The increase in plant-available topsoil P concentrations during the last decades was one of the main factors offsetting yield losses despite P fertilizer omission. Our study suggests that climate change and, in particular, a marked increase in temperature since the middle of the last century most likely influenced soil P dynamics with a significant impact on crop production.

Rice is mainly grown under rainfed conditions in West Africa. Unpredictable and variable rainfall, poor soil quality, and suboptimal crop management practices are the main determinants of low productivity. We assessed the effects of soil water availability and fertilizer application, and their interaction on the yield of rainfed rice in Glazoue, Department of Zou-Collines, central Benin between 2010 and 2013. On-farm fertilizer management trials and field surveys were conducted in 13–39 farmers’ fields per year. Field water conditions were visually assessed three times per week during the rice-growing season and flood and drought indices were calculated on the basis of number of days with ponded water and dry surface soil relative to the total number of days for the vegetative, the reproductive and whole rice-growing period. Variations in flood and drought indices were related to the sand content of the soil. While nitrogen was the most limiting nutrient, average response to N fertilizer application was low with an agronomic N use efficiency of only 7–9 kg grain per kg of N applied. Year-to-year variation in rainfall and spatial variation in field water status affected both rice yield and response to N fertilizer. Some 47% of the observed yield variation was explained by field water status and the amounts of N fertilizer applied, with rice response to N fertilizer being less when water was limited. We conclude that the prevailing blanket fertilizer recommendations are unlikely to contribute to yield increases in rainfed systems of West Africa. There is a need for field-specific recommendations that consider soil texture and the spatial–temporal dynamics of water availability.

Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties' thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO 2 varied between − 6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between − 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers.

Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e. M) to the variability in biomass yield (Y T , total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25 G x M scenarios were simulated at a 5 arc minute resolution (∼5 km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of Y T and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (S i ), showed distinct spatial patterns for the two output variables. The Y T was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of S i to environmental drivers. For example, the sensitivity of Y T and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest.

Rice (Oryza spp.) is the major staple food for most countries in West Africa, but local production does not meet demand. Rice is grown mainly by smallholder farmers, and yields are generally low with high temporal and spatial variability. Low yields have been attributed to unfavorable climate conditions, poor soil quality, and sub-optimum agricultural practices. The objectives of this study were to assess variation in yields of three major rice production systems (irrigated lowland, rainfed lowland, and upland) across three climatic zones (semi-arid, sub-humid, and humid), and identify factors affecting that variation. We analyzed data on yield, climate, soil, and agricultural practices for 1305 farmers’ fields at 22 sites in 11 West African countries between 2012 and 2014. A boundary function approach was used to determine attainable yields. Random forest algorithm was used to identify factors responsible for yield variation. Average rice yield was 4.1, 2.0, and 1.5 t ha−1 in irrigated lowland, rainfed lowland, and rainfed upland systems, respectively, with maximum attainable yields of 8.3, 6.5, and 4.0 t ha−1. Yield difference between attainable and average yield tended to be higher in irrigated and rainfed lowland systems. In those two systems, yields were highest in the semi-arid zone, while no difference in yields among climatic zones was apparent for upland rice. High rice yields were associated with high solar radiation, high maximum temperature, intermediate air humidity, multiple split nitrogen (N) fertilizer applications, high frequency of weeding operations, the use of certified seeds, and well-leveled fields in the irrigated lowland system. Minimum temperature, solar radiation, rainfall, construction of field bunds, varietal choice, and the frequency of weeding operations were determinants of rice yield variation in the rainfed lowland system. Varietal choice, bird control, and frequency of weeding operations affected rice yields in the upland system. Improving access to inputs, improving input use efficiencies, and site-specific management strategies are recommended as priority interventions to boost rice yields at regional scale independent of production system and climatic zone.

We investigated the impact of climate variables on yield and biomass gap variability in two humid topical regions, Brong-Ahafo and Ashanti region, of central Ghana using the crop model LINTUL5 embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management). The simulations were run using a late maturity maize variety ( Obatanpa ) and historical weather data (1992–2007) across the 18 districts of the regions studied. The simulated maize yield and biomass production under water-limited conditions varied spatially which was significantly correlated with the solar radiation and precipitation in the crop growing period (R 2 = 0.99; p 2 = 0.96; p 2 = 0.93; p −1 to 10.0 Mg ha −1 and 14.8 Mg ha −1 to 17.1 Mg ha −1 respectively across the districts. Thus average farmer’s yield and biomass is only 17% and 13% of the simulated water-limited yield and biomass respectively. The spatial and temporal variability in yield gap was positively correlated with the radiation during the crop growing period. Associated spatial variability in biomass gap was positively correlated with radiation and negatively with the precipitation, whereas temporal variability in the biomass gap was positively correlated with the radiation during the crop growing period. Thus, under the current input intensities in humid, tropical Central Ghana, neither maize grain and biomass yields nor the potential water limited yields are significantly positively related to precipitation during the growing cycle. Closing the large yield gaps will require in the first place adequate supply of nutrients.

Large-scale crop simulations with process-based models rely on meteorological input data of coarse spatial resolution. We assess how spatial aggregation of meteorological data to coarser resolutions affects the data’s temporal properties. This is largely unknown as is the impact which this aggregation effect (AE) has on simulations which use such aggregated data as input. In simulations of crop yield AE may exceed 10% in single years. We hypothesize that AE should be analysed with regard to both temporal and spatial input data properties. For this purpose, we analysed changes in temporal multifractal properties of meteorological variables due to spatial averaging from 1 to 100 km resolution. Results show that temporal properties of the time series were affected depending on the meteorological variable. We argue that the magnitude of this effect depends on local orography and climate. Similar impact of spatial aggregation on temporal properties can therefore be expected in regions of comparable orography and climate. These changes in multifractal properties potentially affect results of continuous dynamic simulations.