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Introduction: Optimizing the dynamics of daylily (Hemerocallis citrina Baroni) growth under various planting patterns is critical for enhancing production efficiency. This study presents a comprehensive model to simulate daylily growth and optimize planting patterns to maximize bud yield while minimizing land resource utilization. Methods: The model incorporates source-sink relationship specific to daylilies into physiological process modeling, considering environmental factors such as micro-light and temperature climate, and CO2 concentration. Spatial factors, including planting pattern, row spacing, plant spacing, and plant density were examined for their impact on light interception, photosynthesis, and resource efficiency. Employing partial least square path modeling (PLS-PM), we analyzed the interrelations and causal relationships between planting configurations and physiological traits of daylily canopy leaves and buds. Through in situ simulations of 36 planting scenarios, we identified an optimal configuration (Scenario ID5) with a density of 83,000 plants·ha−1, row spacing of 0.8 m, and equidistant planting with a plant spacing of 0.15 m. Results and discussion: Our research findings indicate that increased Wide+Narrow row spacing can enhance yield to a certain extent. Although planting patterns influence daylily yield, their overall impact is relatively minor, and there is no clear pattern regarding the impact of plant spacing on individual plant yield. This modeling approach provides valuable insights into daylily plant growth dynamics and planting patterns optimization, offering practical guidance for both farmers and policymakers to enhance daylily productivity while minimizing land use. [ABSTRACT FROM AUTHOR]

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d-Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d-Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d-Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d-Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions.

Soil temperature under mulching conditions has a significant impact on crop development, growth rate and other parameters. However, it is not included in the AquaCrop model. Thus, this study aims to improve the AquaCrop model performance for better simulation of soil mulches by considering the heat changing under mulch materials. The proposed modification is conducted through AquaCrop-Open Source software to identify the differences between the temperatures under the mulched soil and air temperatures. It will also help to describe them as additional heat units in specific growth stages. The field data used to evaluate the proposed model has previously been used to calibrate and validate the AquaCrop model in simulating melon growth under different irrigation treatments and soil mulching practices. The results show that the proposed model performs better than the original model in simulating mulched melon under different irrigation regimes. The root mean square error of biomass values was reduced under the modified model by 40%-75% under different irrigation treatments. Additionally, the coefficient of determination (R2) of the modified model slightly increased from the original one. Thus, the proposed model provides a more reliable and robust model.

Crop yield prediction plays an important role in food security. Crop growth models can be used for this purpose, however, they require empirical parametrization. In this study, we show that remote sensing observations can be used to parameterize a crop growth model, providing crop growth monitoring and yield predictions, while mitigating the need for extensive field measurements and accounting for variability in crop varieties. For this purpose, we coupled the SCOPE radiative-transfer model with the state-rate crop model WOFOST. In this paper, we explain how the two models are coupled, assess the accuracy and present a global sensitivity analysis of the coupled model. To assess the accuracy of the coupled model in predicting growth and yield, experimental data of five potato varieties from two fields, in two different locations, in the Netherlands were used. Results show that simulated LAI had a good correlation with measured LAI for four out of the five varieties (R2 > 0.6), while RMSE was in the range 1.09–1.34 m2 m-2. Simulated leaf dry matter content showed similar results, with high accuracy for all varieties (R2 > 0.6) and RMSE between 310 and 440 kg ha-1. Finally, yield simulation showed promising results with an average difference of 1800 kg ha-1 between simulated and measured values. Based on the findings of this study, it is possible to conclude that a coupled WOFOST-SCOPE model has the potential to improve the application of remote sensing data for crop growth monitoring and yield prediction. • Remote sensing data assimilation improves crop growth simulations. • Remote sensing can reduce the dependency on destructive measurements. • Coupling SCOPE and WOFOST address the issue of retrieval continuity. • LAI and leaf weight significantly affect both growth and reflectance simulations. [ABSTRACT FROM AUTHOR]

Drought disasters are natural disasters with complex mechanisms and multiple characteristics. The quantitative evaluation of crop drought risk is central to managing drought risk and reducing loss. Therefore, assessing drought loss risk from the perspective of the drought formation process has scientific and prbactical value. In this study, we constructed crop drought loss risk curves to describe the development of drought risk loss. The intensity of drought events was expressed by the occurrence frequency, which was calculated based on long-term precipitation (1955–2012) and the copula function. The possible loss risk caused by drought was expressed as yield loss, which was simulated using the DSSAT-SROPGRO-soybean model. The spatiotemporal distribution characteristics of agricultural drought loss risk in the Huaibei Plain of Anhui Province, China, were represented by risk distribution maps. The main results showed that: 1) Drought events occurred more frequently in northern regions (Huaibei and Suzhou), but the intensity was lower. 2) Without irrigation, Fuyang was the region with the highest potential drought loss risk (more than 80% yield loss rate). 3) Irrigation had a significant effect on reducing drought risk loss, while efficiency was influenced by the spatiotemporal distribution of precipitation. This study has established and implemented a quantitative framework for regional drought risk assessment by creating drought risk curves and risk maps, which have significant value for improving the regional agricultural drought risk management level. [ABSTRACT FROM AUTHOR]

Introduction: Optimizing the dynamics of daylily (Hemerocallis citrina Baroni) growth under various planting patterns is critical for enhancing production efficiency. This study presents a comprehensive model to simulate daylily growth and optimize planting patterns to maximize bud yield while minimizing land resource utilization., Methods: The model incorporates source-sink relationship specific to daylilies into physiological process modeling, considering environmental factors such as micro-light and temperature climate, and CO2 concentration. Spatial factors, including planting pattern, row spacing, plant spacing, and plant density were examined for their impact on light interception, photosynthesis, and resource efficiency. Employing partial least square path modeling (PLS-PM), we analyzed the interrelations and causal relationships between planting configurations and physiological traits of daylily canopy leaves and buds. Through in situ simulations of 36 planting scenarios, we identified an optimal configuration (Scenario ID5) with a density of 83,000 plants·ha -1 , row spacing of 0.8 m, and equidistant planting with a plant spacing of 0.15 m., Results and Discussion: Our research findings indicate that increased Wide+Narrow row spacing can enhance yield to a certain extent. Although planting patterns influence daylily yield, their overall impact is relatively minor, and there is no clear pattern regarding the impact of plant spacing on individual plant yield. This modeling approach provides valuable insights into daylily plant growth dynamics and planting patterns optimization, offering practical guidance for both farmers and policymakers to enhance daylily productivity while minimizing land use., Competing Interests: The authors declare the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Zhang, Liu, Wu, Zhang and Henke.)

Highlights • Daily modelled NEE of all crop seasons agreed with observations (mean R 2 = 0.7). • The SPA-Crop model was successfully parameterised for pea crops. • At-harvest cumulative NEE had a mean measured-modelled difference of +15%. • Mean yield was overestimated by only +2%. Abstract In support of climate change mitigation strategies and food security, there is an ever-increasing requirement to understand the key drivers of cropland yields and carbon (C) budgets. Process-based crop models, driven by meteorological observations, can provide realistic simulations and diagnosis of the variability in crop C budgets. However, the field-scale observations required to validate the models, particularly across multiple growing seasons and crop types, are seldom available. This research evaluates the Soil-Plant-Atmosphere Crop (SPA-Crop) model for estimating the C fluxes of multiple crop types and seasons at the Swiss long-term flux site Oensingen (CH-OE2). SPA-Crop is a detailed parametric model, simulating ecosystem photosynthesis and water balance at fine temporal (half-hourly time-steps) and vertical scales (multiple canopy and soil layers). We initialised the model at the point-scale using management information and meteorological observations recorded during ten crop seasons from 2004 to 2015. SPA-Crop has been developed for simulating winter cereals (wheat and barley), we further evaluated the model performance for these crops at CH-OE2 and extended the simulation to include a crop-specific calibration for winter rapeseed and pea crops. Model estimates of net ecosystem exchange (NEE) of carbon dioxide (CO 2), ecosystem respiration (R eco), and gross primary production (GPP) were evaluated based on gap-filled and partitioned eddy covariance (EC) fluxes. Further comparisons were made between modelled and observed yields. The daily SPA-Crop estimates had a high agreement with the EC measurements across the different crop types with a mean R 2 of the NEE of 0.74 (wheat), 0.73 (barley), 0.71 (rapeseed) and 0.69 (pea). The modelled yield estimates had biases ranging from −22% (barley, measured-model difference −70 gC m–2; 0.31 t ha−1) to +56% (rapeseed, measured-model difference −85 gC m–2; −0.38 t ha−1), with the largest bias of wheat being only −13% (measured-model difference 34 gC m–2; 0.15 t ha−1) across four wheat seasons. We recommend future SPA-Crop developments – particularly with regards to improving the precision of R eco estimates. Our research demonstrates that SPA-Crop is a reliable tool for simulating the C budget and yield across the four crop types: winter wheat, winter barley, winter rapeseed, and peas. [ABSTRACT FROM AUTHOR]

Agricultural crop production is the most vital sector of the food subsystem within the Food-Water-Energy (FEW) Nexus system, and many environmental and external factors affect it. Complex interactions between FEW subsystems and complex interrelations within each FEW subsystems affect each other dynamically. As a result, in this study, based on the dynamics between FEW subsystems and within FEW subsystems in agriculture, the Agriculture Simulation Model for the Food-Energy-Water nexus (ASM-FEW) is presented at the watershed scale. ASM-FEW includes modules for multi-crop yield modeling and calibration, energy consumption, indices related to FEW, and a comprehensive Water-Energy-Food Nexus Index (WEFNI) to assess crop production efficiency FEW Nexus approach. Modeling evaluation criteria demonstrate the precise crop yield modeling of ASM-FEW for the study area, the Maragheh-Bonab region in the Urmia Lake Basin, from 2005 to 2015. Model evaluation criteria demonstrate precise crop yield modeling. Results show that Potato and rainfed Chickpea exhibit the highest and lowest energy consumption indices, at 222,766 and 19,503 MJ/ha, respectively. Water economic productivity indices indicate irrigated Potato and Wheat as the highest and lowest, with values of 1.084 and 0.114 $/m3, respectively. Energy economic productivity indices reveal that irrigated Onion and rainfed Wheat are the highest and lowest, at 0.132 and 0.006 $/MJ, respectively. Lastly, according to the WEFNI index, irrigated Onion and irrigated Wheat are the most and least suitable crops for cultivation, with values of 0.66 and 0.264, respectively. • A novel framework, ASM-FEW models food-energy-water nexus at agricultural watersheds. • Interrelations within Food-Energy-Water (FEW) subsystems were modeled by ASM-FEW. • AquaCrop was developed for watershed-scale multi-crop simulations in the FEW Nexus. • FEW nexus indices, water, energy, and economic crop efficiency were calculated. • Ranking crops by nexus indices reveal their production advantages and disadvantages. [ABSTRACT FROM AUTHOR]

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d -Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d -Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d -Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d -Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions. • Deficit irrigation conserves water while maintaining sweetcorn and green bean growth and yields. • DSSAT helps in assessing deficit irrigation impact and improving water productivity. • Integrating experiments and crop growth model enhances knowledge of deficit irrigation. [ABSTRACT FROM AUTHOR]

The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

A major challenge of the 21st century is to achieve food supply security under a changing climate and roughly a doubling in food demand by 2050 compared to present, the majority of which needs to be met by the cereals wheat, rice, maize, and barley. Future harvests are expected to be especially threatened through increased frequency and severity of extreme events, such as heat waves and drought, that pose particular challenges to plant breeders and crop scientists. Process-based crop models developed for simulating interactions between genotype, environment, and management are widely applied to assess impacts of environmental change on crop yield potentials, phenology, water use, etc. During the last decades, crop simulation has become important for supporting plant breeding, in particular in designing ideotypes, i.e. 'model plants', for different crops and cultivation environments. In this review we (i) examine the main limitations of crop simulation modelling for supporting ideotype breeding, (ii) describe developments in cultivar traits in response to climate variations, and (iii) present examples of how crop simulation has supported evaluation and design of cereal cultivars for future conditions. An early success story for rice demonstrates the potential of crop simulation modelling for ideotype breeding. Combining conventional crop simulation with new breeding methods and genetic modelling holds promise to accelerate delivery of future cereal cultivars for different environments. Robustness of model-aided ideotype design can further be enhanced through continued improvements of simulation models to better capture effects of extremes and the use of multi-model ensembles. [ABSTRACT FROM AUTHOR]

Motivated by global and regional challenges in food production and a broader consideration of ecosystem services, there has been a substantial increase in the demand for integrated agricultural systems models and spatially-distributed applications that can be used for regional and global assessments and as decision support tools. This demand marks a shift from earlier emphasis in single-crop point simulations and poses a significant challenge as cropping systems models need to improve and increase their capabilities to address multiple scales in a cohesive scientific manner and using updated computing platforms. In this article we discuss how the cropping systems model CropSyst has evolved to meet these new demands and provide some concepts for the future. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) is one of the main agricultural areas in China. However, it is also widely known for its water shortages, especially during the winter wheat growing season. Recently, climate change has significantly affected the water environment for crop growth. Analyzing the changes in the water deficit, which is only affected by climate factor, will help to improve water management in the NCP. In this study, the Decision Support System for Agrotechnology Transfer (DSSAT) was used to investigate the variations in the water deficit during the winter wheat growing season from 1961 to 2010 in 12 selected stations in the NCP. To represent the changes in the water deficit without any artificial affection, the rainfed simulation was used. Over the past 50 years, the average temperature during the winter wheat growing season increased approximately 1.42 °C. The anthesis date moved forward approximately 7-10 days and to late April, which increased the water demand in April. Precipitation in March and May showed a positive trend, but there was a negative trend in April. The water deficit in late April and early May became more serious than before, with an increasing trend of more than 0.1 mm/year. In addition, because the heading stage, which is very important to crop yield of winter wheat, moved forward, the impact of water deficit in late April was more serious to crop yield. [ABSTRACT FROM AUTHOR]

This study evaluated CERES-Rice, AquaCrop, and ORYZA2000 models performance in simulation of biological and grain yield of rice in response to different irrigation intervals and nitrogen levels. These models were calibrated and validated by using three years (2005 to 2007) field experiments. Three levels of irrigation interval included pond treatment, five days interval, and eight days interval, and consisted of four levels of nitrogen. The study results showed that there were significant differences among study crop models in simulation of grain and biological yield in response to different irrigation intervals. As results showed, study models performed more accurate in estimation of rice yield under irrigation intervals than nitrogen levels. All models illustrated high performance in estimation of rice yield under different irrigation intervals. CERES-Rice and AquaCrop models showed highest accuracy in simulation of grain and biological yield of rice under different levels of nitrogen, respectively. In addition, CERES-Rice model indicated highest performance in simulation of grain yield (rRMSE = 16). However, AquaCrop model estimated biological yield more accurate compared to other models (rRMSE = 15). ORYZA2000 showed less accurate in simulating grain (rRMSE = 23) and biological (rRMSE = 21) yield of rice in comparison with other models. [ABSTRACT FROM AUTHOR]

Projecting staple crop production including wheat under future climate plays a fundamental role in planning the required adaptation and mitigation strategies for climate change effects especially in developing countries. The main aim of this study was to investigate the direction and magnitude of climate change impacts on grain yield of rainfed wheat ( Triticum aestivum L.) production and precipitation within growing season. This study was performed for various regions in Khorasan province which is located in northeast of Iran. Climate projections of two General Circulation Models (GCM) for four locations under three climate change scenarios were employed in this study for different future time periods. A stochastic weather generator (LARS-WG5) was used for downscaling to generate daily climate parameters from GCMs output. The Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 was employed to evaluate rainfed wheat performance under future climate. Grain yield of rainfed wheat and precipitation during growth period considerably decreased under different scenarios in various time periods in contrast to baseline. Highest grain yield and precipitation during growth period was obtained under B1 scenario but A1B and A2 scenarios resulted in sharp decrease (by −57 %) of grain yield. Climate change did not have marked effects on evapotranspiration during the rainfed wheat growth. A significant correlation was detected between grain yield, precipitation and evapotranspiration under climate change for both GCMs and under all study scenarios. It was concluded, that rainfed wheat production may decline during the next 80 years especially under A2 scenario. Therefore, planning the comprehensive adaptation and mitigation program is necessary for avoiding climate change negative impact on rainfed wheat production. [ABSTRACT FROM AUTHOR]

The Crop Growth Monitoring System (CGMS) has been applied for spatial biophysical resource analysis of Borkhar & Meymeh district in Esfahan province, Iran. The potentially suitable area for agriculture in the district has been divided into 128 homogeneous land units in terms of soil (physical characteristics), weather and administrative unit. Crop parameters required in the WOFOST simulation model for winter wheat and silage maize, have been calibrated based on experimental data from the study area. The study area has been classified into three cropping calendar zones based on average annual temperature, altitude and latitude. For each zone, a sowing date has been defined for each crop as the starting point of crop growth simulation. Growth of these crops has been simulated for the potential situation in each land unit for 20 years of historical daily weather data. Daily potential evapotranspiration and irrigation requirements of each crop per land unit have been calculated in a postsimulation, on the basis of model outputs. Outputs of the model are crop yield (marketable yield and total biomass) and irrigation requirements per decade. Spatial and temporal variation in irrigation requirements has been analyzed. The temporal variation in crop water requirements is larger than the spati3al variation. [ABSTRACT FROM AUTHOR]

Projecting agricultural crop, such as bread wheat, yield under future climate plays a vital role in planning for supply and demand, especially in developing countries. The objective of this study was to investigate impacts of climate change on grain yield of rainfed wheat in the Kashafrood basin situated in northeastern Iran. Climate projections of two General Circulation Models (HadCM3 and CGCM2) under two climate change scenarios (A2 and B2) were employed in this study. A statistical downscaling method was employed for developing the quantitative relationship between large-scale atmospheric variables (predictors) and local variables (observations), also daily climatological parameters performed by the LARS-WG5 stochastic weather generator, the Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5 adjusted to evaluate rainfed wheat performance. Results of this study represented substantial difference between GCM models and scenarios for wheat yield in the study area. The A2 scenario indicated more negative impact on wheat yield than the B2 using both GCM models. Highest increase and decrease of grain yield in comparison with the baseline belongs to the 2010-2039 period (+15%) using the HadCM3 model under the B2 scenario and the HadCM3 model under A2 in the 2040-2069 period (−50%), respectively. In general, growth period length indicated a markedly decreasing trend in both periods. There were strong relations between wheat yield and precipitation rate and growth period length in this study. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Abstract: In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L.) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kgha−1), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (−1159kgha−1). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. [Copyright &y& Elsevier]

Development and evaluation of mitigation strategies are fundamental to manage climate change risks. This study was built on (1) quantifying the response of maize ( Zea mays L.) grain yield to potential impacts of climate change and (2) investigating the effectiveness of changing sowing date of maize as a mitigation option for Khorasan Province which is located in northeast of Iran. Two types of General Circulation Models (GCM: (United Kingdom Met Office Hadley Center :HadCM3) and (Institute Pierre Simon Laplace: IPCM4)) and three scenarios (A1B, A2 and B1) at four locations (Mashhad, Birjand, Bojnourd and Sabzevar) employed in this study. Long Ashton Research Station-Weather Generator (LARS-WG) was employed for generating the future climate. The Cropping System Model (CSM)-CERES-Maize was used for crop growth simulation under projected climate conditions. The results showed the simulated grain yields of maize gradually would decrease (from −1% to −39%) during future 100 years compared to baseline under different scenarios and two GCM at all study locations. The simulation results suggested that delayed sowing date from May to June at all study locations, except Sabzevar location is the most effective mitigation option for avoiding thermal stress at end of growth period. In addition, shifting in sowing date to March or April will be beneficial in terms of obtaining higher yields in Sabzevar. Grain yield did not show special trend from north to south of Khorasan Province in the future climate. In general, change of sowing date may be quite beneficial to mitigate climate change impacts on grain yield of maize in northeast of Iran. [ABSTRACT FROM AUTHOR]

Abstract: In research to date, projected climate change has been considered to be beneficial for agriculture under Nordic conditions, where crop production is mainly limited by low temperatures resulting in short growing seasons. However, with the rapid increases in global mean temperature implied at the high end of the uncertainty range of current projections, which are typically amplified at high latitudes, conditions for crop production could change so dramatically that yields would be reduced, even accounting for the positive effects of CO2 fertilization. In this study, we used the WOFOST crop growth simulation model to examine crop yield responses to a set of plausible scenarios of climate change for Finland up to 2100, including some that exceed 4°C global mean temperature increase relative to pre-industrial. We selected spring barley (Hordeum vulgare L.) as an indicator crop and calculated water-limited yields for two Finnish locations, Jokioinen and Jyväskylä and for a clay and a sandy soil. Scenarios included systematic increases in temperatures, changes in precipitation distribution and altered daily climatic variability using the M&Rfi weather generator. We also examined the effectiveness of a few adaptation options, such as shifts in sowing dates and hypothetical new crop cultivars. Increasing temperature reduced total growth duration and yield considerably, even with adjusted earlier sowing. A reduced number of rainy days had marked negative effects only in combination with increases in temperature of 4°C or greater, leading to distinctly higher yield losses on the sandy soil than on the clay. Prolonged dry spells clearly increased yield variability. For scenarios with temperature increases of +6°C and +7°C, yield losses at Jokioinen were highest; losses at Jyväskylä were generally less pronounced. Neither CO2 fertilization nor adjusted sowing could compensate the yield losses from temperature changes exceeding +4°C. On clay soils, yield loss could be compensated by new cultivars. For sandy soils even with new cultivars, there would be yield loss at temperature increases exceeding +3°C. It can be concluded that the positive effects of climate warming and elevated CO2 concentrations on cereal production at high latitudes are likely to be reversed at temperature increases exceeding 4°C, with a high risk of marked yield loss. Only plant breeding efforts aimed at increasing yield potential jointly with drought resistance and adjusted agronomic practices, such as sowing, and adequate nitrogen fertilizer management and plant protection, holds a prospect of partly restoring yield levels and reducing the risks of yield shortfall. [Copyright &y& Elsevier]

Abstract: We used the modeling package Expert-N to investigate the ability of four generic-mechanistic crop models that were originally developed under field conditions to simulate the plant growth of spring wheat grown in open-top chambers (OTC) under different environmental conditions. We focus on the impacts of water limitation and elevated atmospheric CO2 concentration on biomass production. Expert-N facilitates the comparison of the components of agro-ecosystem models because it allows the exchange of single modules while leaving the rest of the model unchanged. The crop growth part of the models SPASS, CERES-Wheat, SUCROS and GECROS were combined with the Penman–Monteith equation for potential evapotranspiration, the HYDRUS-1D model for water transport and the LEACHN model for nitrogen transport and turnover simulation. The models were applied to a data set provided by OTC experiments with spring wheat (Triticum aestivum L. cv. ‘Minaret’) that was grown under two atmospheric CO2 concentrations (ambient/elevated), two irrigation schemes (non-limited water supply/water limitation) and two soil types (Cambisol/Chernosem) in two subsequent vegetation periods (1998/1999). Based on the model calibration using experimental and literature data, the best simulation results describing the impact of the considered environmental conditions were obtained using the SUCROS model followed by the SPASS, GECROS and CERES models. The study depicts the shortcomings of the underlying processes in all of the models. These shortcomings need to be addressed when models are applied on regional scales or for prediction under climate change conditions. [Copyright &y& Elsevier]

Abstract: Natural capital (land, water, air) consists of many resources, each with its own quality, dynamics and renewability, but with strong interactions. The increasing competition for the natural resources, especially land and water, calls for a basic redirection in the analysis of land use. In this paper, the land use analysis cycle is introduced and recent methodological developments for supporting some of its distinct phases are illustrated. Tools are available to quantify the production possibilities for various crops at plot or field scale, though the accuracy of the estimates decreases as less of the production factors are under control. Illustrations are given for quantitative analysis of management options at the farm level. In terms of explorative studies these tools appear highly successful and where the socio-economic environment is conducive, results of such analysis can indeed form the basis for formulation of development options. Tools have been developed for quantitative analysis at (sub-)regional level, that allow exploration of the outer envelope of development possibilities. The examples still largely bear an academic character, but as the demand by policy makers for integrated land use analysis studies increases, they may serve as building blocks for development of operational methodologies for land use policy formulation and analysis. Their potential impacts on planning procedures and achievement of land use objectives are high, particularly when they are further developed in settings that allow participation and involvement of the various user groups. [Copyright &y& Elsevier]

Abstract: Located in the Aral Sea Basin, Uzbekistan suffers from environmental problems related to soil salinization and water scarcity. The arid climate requires that cultivated crops be intensively irrigated. Under conditions of limited resources, crop production must be maintained at expense of minimum inputs but aiming at achieving maximum returns. The search of the best combination between the available resources and crop yield can be eased by the use of system modelling. In this work, the Root Zone Water Quality Model (RZWQM) was used. The research was conducted in an experimental field located in the Fergana Valley, Usbekistan. The soil is a Sierozem cropped with maize (Zea maïs) and irrigated with saline water. The soil hydraulic and crop growth components of the model were calibrated against field data. Initial values for the soil hydraulic properties were determined by a combination of the in situ method of the soil monolith and two alternative laboratory methods. After calibration, the model was run as a management tool to predict the impact of different irrigation and fertilization practices upon crop yield. Soil moisture, soil water potential, water table depth and plant development were monitored during the 2001 growing season and used for model evaluation. Results from the comparison of field and simulated data show a good calibration of the soil hydraulic and plant growth model components. Soil water was simulated for five soil layers with an average deviation from the measured values of 3.6%. Crop yield was estimated by the model with an error of 13%. The results from the simulation of various agricultural management scenarios can be used for further agro economical analysis and assessment in view of economic profitability. Nevertheless, other model components like the chemical modules must be calibrated against detailed field experiments. [Copyright &y& Elsevier]

Abstract: Severe land degradation affects the livelihood of many farmers in the highlands of Tigray, northern Ethiopia. Various soil and water conservation practices have been proposed to reduce land degradation and to improve the quality of the natural resource base but quantitative information on their agro-ecological effects is often lacking. In this study, effects of three soil and water conservation practices are assessed using a crop growth simulation model (WOFOST), a nutrient monitoring model (NUTMON) and a hydrological erosion model (LISEM), which are applied at field, farm and regional scale, respectively. Evaluated soil and water conservation practices include (i) bunds along field contours to improve water availability for crop production, (ii) mulching of crop residues to improve soil nitrogen stocks and (iii) reforestation to reduce erosion. Data from the watershed Gobo Deguat in northeastern Tigray have been used as a case-study. Bunds slightly increased crop productivity at sowing dates when water-limited yields were low, while productivity decreased at more favorable sowing dates due to the reduction in cropped area required for the construction of bunds. Crop residues used as mulch hardly contributed to lower soil nitrogen depletion at farm level as the total amount of nitrogen in crop residues was limited, while part of the crop residue nitrogen returned to the soil anyway in the form of manure and compost. Reforestation reduced erosion up to 14% but this is insignificant compared to the sacrifice in cultivated land which needs to be reforested. Applied tools allow rapid ex-ante evaluation of soil and water conservation practices and may contribute to improved cost-benefit analysis of proposed measures, and identification of more appropriate means to combat land degradation. The analyses show important trade-offs and the effects of the assessed soil and water conservation practices may partially explain their low rate of adoption in Tigray. [Copyright &y& Elsevier]

In this research, the performance of satellite rainfall estimates (SREs) for crop growth simulation was investigated. Rainfall products selected were CHIRPS 2.0, CMORPH 1.0, MSWEP 2.2, and RFE 2.0. In-situ rainfall from 20 stations within the Lake Victoria basin in Kenya served as reference. Rainfall products were evaluated for onset days, rainfall depths, dry spells, and rainfall occurrence for four crop growth stages. Assessment was on a daily time step for the period 2012–2018 and on a point-to-pixel basis. Results showed that SREs exhibit large variation in timing of rainfall arrival. SREs exhibited largest interannual and spatial spreads in representing dry spell length during the flowering stage with CMORPH and CHIRPS showing best and weakest results, respectively. Bias of SREs in representing dry spells was smaller during early growth stages. Detecting rainfall occurrence by the SREs weakened as the growing season progressed. MSWEP, followed by RFE2, produced the best results in detecting rainfall events, while falsely detected rainfall was frequent in CHIRPS, particularly in later growth stages. SREs generally performed better during a wet than a dry growing season. SREs indicated less bias in rainfall depths during the early stages of crop growth but deteriorated at later stages. MSWEP and CMORPH exhibited the least and highest interannual spread in relative bias, respectively. In associating biases to severe and extreme water stress, based on crop water requirement satisfaction index, effects were more prevailing in the ripening than flowering stages. Findings of this study suggest that SREs can serve as input to crop growth modelling, but validation of SREs with rain gauge observed counterparts is essential. • Timing of rainfall arrival by satellite rainfall products (SREs) widely varies. • SRE's bias in representing dry spells is smaller during early crop growth stages. • SREs perform better during a wet than a dry growing season. • Performance of SREs weakens as the crop growing season progresses. • SREs' error propagation in crop water stress is more prevailing in ripening that flowering stages. • SREs can serve as crop growth simulations input but validation is essential. [ABSTRACT FROM AUTHOR]

• Eight barley models and eight climate projections for the 2050s were applied at two sites. • Sensitivity analyses were conducted on the responses of major crop processes to major climatic variables. • A new comprehensive analysis was conducted to look at the reasons why crop models diverge substantially. • Impacts of increases in temperature and CO 2 on leaf area development were the major causes. • The study has important implications for models improvement and experimental design. 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 CO 2 , as well as their interactions, for each of the eight crop models. The results showed that the temperature and CO 2 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 CO 2 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 CO 2 increases will affect leaf area development, crop evapotranspiration, photosynthesis, and grain formation in contrasting environments are needed for modeling their impacts. [ABSTRACT FROM AUTHOR]