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Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model. [ABSTRACT FROM AUTHOR]

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The Campos Gerais region, located in the Subtropical Zone in Southern Brazil, stands out for presenting agricultural yields above the national average, especially for soybean. This study aimed to calibrate and validate the AquaCrop model for soybean crop in the edaphoclimatic conditions of Campos Gerais region. The data used were from the experimental stations of ABC Foundation in Arapoti, Castro and Ponta Grossa, Parana State, and Itaberá, São Paulo State. The input data (climate, crop, soil and soil management) from the 2006/07 to 2015/16 harvests were collected at the respective experimental stations and entered in the AquaCrop model for yield simulations. The data for model calibration were different from those used in the validation. The observed and simulated productivities were evaluated by simple linear regression analysis, mean absolute and relative errors, Pearson correlation coefficient (r), agreement (d) and performance (c) indexes. The model calibration was satisfactory in the studied localities, with agreement indices ranging from 0.87 < d < 0.99. In the validation, the model performance index ranged from "Terrible" to "Excellent", with agreement ranging from 0.59 < d < 1.00. The results showed a good relationship between the observed and simulated yields, indicating that the AquaCrop model is an option to plan and investigate alternatives that improve soybean crop productivity in the Campos Gerais region. [ABSTRACT FROM AUTHOR]

IntroductionCrop simulation models are very useful tools for the evaluation of plant growth and development processes. Crop-simulating models may be used to estimate yield and evaluate climatic, plant, and management parameters on yield. Also, it may be used to predict crop water requirements under different conditions. Crop models should be evaluated and parameterized to simulate crop growth and development. Parameterization is used for precise simulation of crop growth and development and can estimate the best and most appropriate values for model parameters obtained via observed data or calibration. The objectives of this study were to describe the SSM-iCrop2 model, determine plant parameters, and evaluate alfalfa (Medicago sativa L.) in its major production regions using the SSM-iCrop2 model in Iran.Materials and MethodsSSM-iCrop2 crop simulation model is a simplified form of SSM crop models which is suitable for the simulation of growth, development, and yield of different crops under different environmental conditions and large-scale estimation of crop production, especially in the evaluation of nutrient availability and climatic effects. This model includes sub-models of phenology, leaf expansion and senescence, dry matter production and distribution, and soil water balance. Daily weather data, agronomic management, soil properties, and plant parameters are required for simulation in this model. The present study investigates the performance of the SSM-iCrop2 model regarding the prediction of single cuts and overall cuts, phonologic stages, and water requirement of alfalfa under changing climatic conditions in Iran. To simulate the growth, development and yield of alfalfa using SSM-iCrop2 model in Iran, the major irrigated alfalfa production provinces, including East Azarbaijan, Hamedan, West Azarbaijan, Sistan and Baluchestan, Khorasan Razavi, Esfahan, Kordestan, Ghazvin, Ardabil, Markazi, Fars, Zanjan, Chaharmahal and Bakhtiyari and Tehran were identified based on the data available in Ministry of Agriculture statistics. Then, field experiment data required for model parameterization and estimation were collected from these provinces.Results and DiscussionAccording to the results of the SSM-iCrop2 model parameterization, two cultivars with different leaf area indices (high-yielding and low-yielding) were identified in major alfalfa production provinces. The model was evaluated using independent experimental data that had not been used for parameterization. The evaluation results for alfalfa yield showed that the observed single-cut forage yield ranged from 112 to 640 g.m-2 with an average of 330 g.m-2; the observed total annual forage yield ranged from 646 to 4042 g.m-2 with an average of 1717 g.m-2; and the water requirement of alfalfa obtained from the NETWAT software was between 5140 to 12690 m3 ha-1 with an average of 8746 m3 ha-1. The predicted single-cut forage yield, predicted total annual forage yield, and alfalfa water requirement ranged from 189 to 457 g.m-2 with an average of 351 g.m-2, 693 to 3296 g.m-2 with an average of 1654 g.m-2, and 4093 to 16874 m3 ha-1 with an average of 10940 m3.ha-1, respectively. Overall, in the evaluation of observed versus simulated alfalfa forage yield, 31 points were obtained for single-cut yield with a correlation coefficient (r) of 0.79, root mean square error (RMSE) of 88.3 g.m-2, and coefficient of variation (CV) of 26.78%; and 21 points were obtained for annual yield with an r of 0.90, RMSE of 344.4 g.m-2, and CV of 20.05%. The evaluation results also showed that the observed versus simulated alfalfa water requirement had an r of 0.43, RMSE of 3503 m3 ha-1, and CV of 40%.Conclusion The results obtained from parameterization and evaluation of the SSM-iCrop2 model show that the mentioned model presents a logical prediction and accurate estimation of model parameters for yield and water requirement of alfalfa crops in Iran. Thus, this model may be used for the prediction of alfalfa yield under different climates and management conditions.

Floods and droughts are the events that most threaten crop production; however, the impact of floods on crops is still not fully understood and often under-reported. Nowadays, multiple sources of information and approaches support the estimation of agricultural losses due to floods. This study aims to understand the differences in agricultural loss estimates provided by two conceptually different approaches (crop models and expert-based models), evaluating their sensitivity to flood hazard inputs. We investigated the challenges in flood agricultural loss assessments referring to a case study for which, in addition to model simulations, information from surveys and on-site inspections were available. Two crop models (APSIM and WOFOST) and the expert-based model AGRIDE-c were applied to evaluate agricultural yield losses after the flood event of the Panaro River (Emilia-Romagna, Northern Italy) that took place on the 6 December 2020. Two modelling tools were used to reproduce the event: the hydraulic model HEC-RAS and the image-based tool FwDET. Additionally, surveys among local farmers were conducted in the aftermath of the event to evaluate the flood features (water depth, extent and duration) and crop losses. The main findings of the study are that APSIM and WOFOST provide similar estimates of yield losses, while AGRIDE-c tends to underestimate yield losses when the losses over the entire study area are evaluated. The choice of the flood simulation technique does not influence the loss estimation since the difference between the yield loss estimates retrieved from the same model initialized with HEC-RAS or FwDET was always lower than 2%. Information retrieved from the surveys was not sufficient to validate the damage estimates provided by the models but could be used to derive a qualitative picture of the event. Therefore, further research is needed to understand how to effectively incorporate this kind of information in agricultural loss estimation. [ABSTRACT FROM AUTHOR]

With limited land resources and a growing population, agricultural output is under considerable strain. New technology is necessary for overcoming these issues and advising farmers, legislators and other decisionmakers on adopting sustainable agriculture despite global climate variations. This has led to the crop simulation models that illustrate crop growth and development processes as a function of climate, soil and crop management. They also support agricultural agronomy (yield estimate, biomass, etc.), pest control, breeding and natural resource management. This study examines crop modelling for agricultural production planning and field-level management strategies. These can help researchers comprehend the significance of crop modelling for scenario-building and provide field-level suggestions by analysing future conditions and strategic activities to minimize the predicted negative influence and maximize the projected positive effect. The limitations and potential directions of crop modelling improvement have also been highlighted in this study. [ABSTRACT FROM AUTHOR]

Climate change has an impact on soybean production in the USA, necessitating thorough impact studies across broad geographic areas and extended periods to develop appropriate coping strategies. This study investigates the simulated response of soybean in ten major soybean-growing states of the USA under Climate Model Intercomparison Project Phase 5 based on multiple global climate models, two representative concentration pathways [RCP8.5 and RCP4.5] under rainfed and irrigated conditions for 2013–2039, 2043–2069, 2063–2099. The future climate series was developed using Agricultural Model Intercomparison and Improvement Project protocol by applying mean and variability, and CROPGRO-soybean model was explored for soybean simulation under 400 ppm CO2 level and a set of management. Under future climate, intense changes in temperature, precipitation amount and variability are anticipated under RCP8.5 than RCP4.5. As a result, a shorter life cycle, more evapotranspiration, lower grain production, higher water consumption and water productivity were expected under RCP8.5 than RCP4.5 scenarios. A higher reduction in grain yield and water productivity is expected under rainfed than irrigated conditions and intensity increases with advancement towards end of the century. Irrigation tends to decrease adverse climate change effects; however, the marginal economy for irrigation water must be assessed. Since the northern states under study are likely to experience increased grain yields or lower negative impacts, these areas could be the major production zones for soybean production in the future if only climate change is taken into account. Before reaching a convincing conclusion, different adaptation strategies must be thoroughly investigated. [ABSTRACT FROM AUTHOR]

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively. [ABSTRACT FROM AUTHOR]

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield. [ABSTRACT FROM AUTHOR]

Africa is facing an urgent need to increase food production to meet increasing demands. Targeted investments in integrated agriculture and, water management systems are required to meet this challenge. However, there is a lack of comprehensive information on the potential applications of climate-smart agriculture (CSA). This paper reviews current crop modeling technologies and their applications within the scope of climate change and the CSA framework in Africa. It evaluates current research trends in various crop simulation models and suggest advanced approaches to improve crop and environmental assessment, crop management, and decision-making. A total of 140 relevant papers were considered. Results showed that 84% of studies used process-based models, with Maize being the most studied crop. Additionally, DSSAT crop models and analysis of variance models have the highest contribution of physical and empirical crop modeling studies respectively. Over 72% of studies have contributed to adaptation strategies and reducing yield gaps, while only 8% of studies have been conducted on climate change mitigation and their trade-offs with adaptation using crop models under CSA. To ensure food security through sustainable agricultural practices in Africa, there is crucial to implement CSA models with a focus on the climate change mitigation component.

Process-based soil-crop models are widely used in agronomic research. They are major tools for evaluating climate change impact on crop production. Multi-model simulation studies show a wide diversity of results among models, implying that simulation results are very uncertain. A major path to improving simulation results is to propose improved calibration practices that are widely applicable. This study proposes an innovative generic calibration protocol. The two major innovations concern the treatment of multiple output variables and the choice of parameters to estimate, both of which are based on standard statistical procedure adapted to the particularities of soil-crop models. The protocol performed well in a challenging artificial-data test. The protocol is formulated so as to be applicable to a wide range of models and data sets. If widely adopted, it could substantially reduce model error and inter-model variability, and thus increase confidence in soil-crop model simulations. • A new protocol for crop-soil model calibration is proposed. • The choice of parameters to estimate is based on statistical model selection. • Multiple variables are fit simultaneously using weighted least squares. • The protocol was tested using artificial data with multiple observed variables. • The calibrated model worked well including for new environments. [ABSTRACT FROM AUTHOR]

Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using "simpler" statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species. [ABSTRACT FROM AUTHOR]

A simplified crop model with an assimilates buffer (labelled 2S) is developed and compared to a more traditional model without a buffer (1S). The study explores the effects that the different model formulations have on the optimal control of the greenhouse environment. Values of the parameters specific to the 2S model are estimated from information in the literature. The size of the buffer is estimated in two different ways. Based on my previous modelling experience, a constant buffer co-state (virtual value of assimilates) is chosen to guide the control decisions. Its value is also estimated in two different ways: Calculated optimal values of the assimilates co-state, for various model parameter sets, turn out to be similar to values based on the proportion between assimilates and fresh fruit. Results for Dutch conditions and typical control equipment, show that the 2S optimal control strives to achieve balanced (sink = source) growth in summer, by increasing temperature and restricting CO 2 enrichment relative to the 1S control, where growth is always balanced (by definition). Utilizing 2S also improves the positive correlation between mean daily temperature and mean daily global radiation, a correlation which could be used to guide the control in practice. • Two crop growth models are developed: with and without an assimilates buffer. • Parameters quantifying buffer size and assimilates partitioning are estimated. • A constant buffer co-state is used to optimize greenhouse environmental control. • Optimal control strives to achieve balanced growth. • Control of model with buffer boosts sink strength at the expense of source strength. [ABSTRACT FROM AUTHOR]

Coffee-based farming systems (CBFS) support smallholder farmers through mainly coffee growing with integration of other food crops and livestock. Climate change is expected to ravage crop suitability in several agroecological zones, posing a threat to national earnings and livelihoods. However, previous studies have mainly considered crop-specific analyses rather than the major crops in a farming system. This study illustrates variations in climatic suitability of major crops grown in Uganda’s Arabica and Robusta CBFS at disaggregated altitudes. Climate data (1980–2009) was projected for 2010–2039 (near-term future) for five climate scenarios under Representative Concentration Pathways—RCP 8.5 and 4.5 using twenty-nine global climate models (GCMs) based on the delta method. Climatic suitability of coffee, banana, maize, and beans was assessed using EcoCrop model. Rainfall and temperature changes are expected during long rains and second-dry seasons, with higher rainfall increments during short rains. Minimum temperatures are likely to increase in low altitudes under ensemble-mean, hot-wet, and hot-dry scenarios. Crop suitability improvements (> 5% area) are expected in mid to high altitudes under cool-wet and hot-wet, mainly for RCP 4.5 while western Uganda Arabica CBFS are unlikely to experience crop suitability changes. Suitable area for East African banana and beans is likely to increase utmost 44.7%, and expected to decline to marginal utmost 64% (coffee and banana) and 21.2% (maize) in central Robusta and eastern Arabica CBFS under ensemble-mean, cool-dry, and hot-dry scenarios. Plantain and dessert banana are likely to become unsuitable within Robusta and high-altitude Arabica CBFS. This study recommends identification and use of system appropriate climate-smart adaptation strategies to mitigate future crop-climate vulnerabilities within CBFS. [ABSTRACT FROM AUTHOR]

Deficit irrigation or intentional under-irrigation offers the potential for sustainable water resources management. The DSSAT CERES-Maize and AquaCrop models were coupled to simulate the effects of deficit irrigation on corn yield and water productivity. The models were calibrated and validated using observed values of crop and biomass yield under 40%, 50%, 60%, 70%, and 80% depletion of the available soil water. Model simulation results showed that a 15% level of deficit irrigation results in maximum yield while a 60% level of deficit irrigation leads to maximum water productivity. Results suggest that it is not necessary to use large amounts of water in order to obtain high crop yield. The net irrigation application depths ranged from 60 mm to 134 mm, with a depth of 77 mm as optimum under 60% deficit irrigation when applied at the start of tasseling to grain filling. This study demonstrated the applicability of deficit irrigation as a water-saving management strategy for corn production systems. Crop models such as DSSAT CERES-Maize and AquaCrop proved to be viable tools to support decision making in corn production systems in the Philippines, especially when employing deficit irrigation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the past century, climate change has become more significant, which has a great impact on crop growth, especially food security. Based on the regional climate model PRECIS, high-precision grid climate data in China under RCP4.5 and RCP8.5 scenarios were output, and the high-precision amplification and calibration of crop model DSSAT were calibrated and verified in combination with data of maize planting from 2005 to 2015, including observation data of agrometeorological stations, ecological networking experiment data and maize survey data of agricultural demonstration counties. The impact of climate change on maize production in 2030s and 2050s was evaluated; and the effect of main adaptation strategies to climate change is put forward which could support macro strategies of layout adjustment for the maize production system. The results show that if no countermeasures are taken in the future, the risk of maize yield reduction in China will gradually increase, especially under the RCP8.5 scenario. The risk of maize yield reduction in each main production area will be very prominent in the 2050s under the RCP8.5 scenario, which would be between 10–30%. Compared with a delayed sowing date, an early sowing date would be more conducive to maize production, but there would be some differences in different regions. The heat in the growing season of maize would increase significantly. If the growth time of maize from silking to maturity could be prolonged and the accumulated temperature could be raised, the dry matter accumulation of maize would effectively increase, which would have an obvious effect on yield. Improving grain filling rate is also significant, although the effect of yield increase would be smaller. Therefore, sowing in advance, full irrigation and cultivating varieties with a long reproductive growth period could effectively alleviate the yield reduction caused by climate change. Adjusting maturity type and grain harvest strategy would have a more obvious mitigation effect on yield reduction in northeast China and northern China, and plays a positive role in ensuring future maize yield. [ABSTRACT FROM AUTHOR]

Floods and droughts are the events that most threaten crop production; however, the impact of floods on crops is still not fully understood and often under-reported. Nowadays, multiple sources of information and approaches support the estimation of agricultural losses due to floods. This study aims to understand the differences in agricultural loss estimates provided by two conceptually different approaches (crop models and expert-based models), evaluating their sensitivity to flood hazard inputs. We investigated the challenges in flood agricultural loss assessments referring to a case study for which, in addition to model simulations, information from surveys and on-site inspections were available. Two crop models (APSIM and WOFOST) and the expert-based model AGRIDE-c were applied to evaluate agricultural yield losses after the flood event of the Panaro River (Emilia-Romagna, Northern Italy) that took place on the 6 December 2020. Two modelling tools were used to reproduce the event: the hydraulic model HEC-RAS and the image-based tool FwDET. Additionally, surveys among local farmers were conducted in the aftermath of the event to evaluate the flood features (water depth, extent and duration) and crop losses. The main findings of the study are that APSIM and WOFOST provide similar estimates of yield losses, while AGRIDE-c tends to underestimate yield losses when the losses over the entire study area are evaluated. The choice of the flood simulation technique does not influence the loss estimation since the difference between the yield loss estimates retrieved from the same model initialized with HEC-RAS or FwDET was always lower than 2%. Information retrieved from the surveys was not sufficient to validate the damage estimates provided by the models but could be used to derive a qualitative picture of the event. Therefore, further research is needed to understand how to effectively incorporate this kind of information in agricultural loss estimation.

This document is an editorial published in Frontiers in Genetics titled "Enriching genomic breeding with environmental covariates, crop models, and high-throughput phenotyping." The editorial highlights the advancements in genomic breeding that have been made through the integration of diverse data sources and methodologies. The special edition features six articles that address key aspects of modern plant breeding, including genomics, enviromics, and phenomics. The articles showcase successful applications of high-throughput phenotyping, genomic-assisted breeding, environmental information, and crop growth modeling. The editorial serves as a valuable resource for researchers, practitioners, and policymakers interested in data-driven approaches to sustainable and productive agriculture. [Extracted from the article]

Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. [ABSTRACT FROM AUTHOR]

Soil water and fertility management have been the main challenges of crop production in West Africa, and their impacts are exacerbated by climate variability. While research has been conducted to optimize fertility and water applications for rainfed crops production in this region, little is known about the management of these resources for off-season cereal crops production. This study assessed the optimal combination of irrigation and fertilizer levels for off-season maize production in Benin, using the DSSAT CERES-Maize crop model. Two years' experiments (2018 and 2019) of 4 levels of deficit nutrient (DN) and two years' experiments (2019 and 2020) of 4 levels of deficit irrigation (DI) were conducted and data were collected on maize growth and yield. DSSAT model was calibrated using crop data from DN experiment in 2018 (DN2018) and DI experiment in 2019 (DI2019), and validated using the DN2019 and the DI2020 experimental data. Then, a long-term scenarios analysis (40-years, 1980–2019) was performed to optimize (i) DI levels, (ii) DN rates; and (iii) combined DI levels and DN rates. The model predicted the grain yield (GY) and total aboveground biomass (TB), with a relative root mean square error and a coefficient of efficiency of 18.3 % and 0.38 for the GY and 11.7 % and 0.50 for the TB during the validation, respectively. However, the model did not account for the effects of DI or DN on the phenological dates, which led to similar predicted values for the anthesis and maturity dates among DI and DN treatments during calibration and validation. Moreover, the model was sensitive to periods with high values of temperature (>45°C) recorded during the DI period, inducing a reduction of the grain filling rate in DI treatments. DI treatments were more sensitive to a change in DUL, SLL, SAT, RGFIL and RUE than the DN treatments; while the DN treatments were more sensitive to the CTCNP2. Reducing maize water requirements by 40 % at the vegetative stage resulted in similar predicted grain yield as in the full irrigation treatment; while reducing the water requirements by 60 % resulted in similar predicted water use efficiency (WUE) as in the full irrigation treatment. Furthermore, the inter-annual variability of grain yield was lower under the optimal DI combined with no fertilizer but higher under high DI combined with higher fertilizer rates. Finally, a combination of 40–60 % of deficit irrigation at the vegetative stage and one-third to half of the recommended fertilizer rates depending on resources availability was the optimum combination of DI and DN rates for off-season maize production. The projected grain yield and WUE under optimal DI and DN levels were likely underestimated due to shortcomings in the model structure to deal with effects of water and nutrient stresses on phenological dates. For reliable assessments of the effects of water and nutrient stresses on grain yield and WUE, there is need to update parameterization and code of the CERES crop models in DSSAT to have a sufficiently strong effect of water and nutrient stress on phenological dates, and the contribution of phenology to LAI and yields predictions. • Irrigation can be an alternative to rainfed production to improve food security. • Medium deficit irrigation levels induced similar grain yield as full irrigation. • Reducing maize water requirements by 60 % induced same WUE than no reduction. • The contribution of crop phenology to yield prediction in DSSAT should be improved. [ABSTRACT FROM AUTHOR]

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

In California, Brassica oilseeds may be viable crops for growers to diversify their cool-season crop options, helping them adapt to projected climate change and irrigation water shortages. Field trials have found germination and establishment problems in some late-planted canola, but not camelina at the same locations. We used computer modeling to analyze fall seedbed conditions to better understand this phenomenon. We found seedbeds may be too dry, too cold, or both, to support germination of canola during late fall. Based on seedbed temperatures only, canola should be sown no later than the last week of November in the Central Valley. Camelina has broader temperature and moisture windows for germination and can be sown from October to December with less risk, but yields of camelina are lower than canola yields. In areas without irrigation, growers could plant canola opportunistically when seedbed conditions are favorable and use camelina as a fallback option.

Many studies have estimated the effect of climate change on crop productivity, often reflecting uncertainty about future climates by using more than one emissions pathway or multiple climate models, usually fewer than 30, and generally much fewer, with focus on the mean changes. Here we examine four emissions scenarios with 720,000 future climates per scenario over a 50-year period. We focus on the effect of low-frequency, high-impact weather events on crop yields in 10 countries of Southern Africa, aggregating from nearly 9,000 25-kilometer-square locations. In the highest emissions scenario, median maize yield is projected to fall by 9.2% for the region while the 5th percentile is projected to fall by 15.6% between the 2020s and 2060s. Furthermore, the frequency of a low frequency, 1-in-20-year low-yield event for rainfed maize is likely to occur every 3.5 years by the 2060s under the high emissions scenario. We also examine the impact of climate change on three other crops of considerable importance to the region: drybeans, groundnuts, and soybeans. Projected yield decline for each of these crops is less than for maize, but the impact varies from country to country and within each country. In many cases, the median losses are modest, but the losses in the bad weather years are generally much higher than under current climate, pointing to more frequent bouts with food insecurity for the region, unless investments are made to compensate for those production shocks.

• Maize growth models differ widely in their simulations of soil temperature. • Root-mean-square errors ranged from about 1.5 °C to 5.1 °C. • APSIM, ecosys, JULES, Expert-N, SLFT, and maizsim were the best six models. • Most of the best models used a numeric energy balance on each soil layer. • More recent soil thermal conductivity routines might help improve the models. Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize (Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method "flavors", so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models. [Display omitted] [ABSTRACT FROM AUTHOR]

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1 ) or leaf appearance rate (LAR; leaf o C-day−1 ). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2=0.99 vs. 0.95, n=4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9±7.5°C-day, 9.8±1.2 leaves, and 30.9±5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r=0.69), while photoperiod was positively related to days to flowering or total leaf number (r=0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models. [ABSTRACT FROM AUTHOR]

Agricultural system models are tools that provide a synthesis and quantification to evaluate the effects of water, soil, crops, management practices, and climate on the sustainability of agricultural production and to ensure food security. Present-day agricultural models are the outcomes of research initiatives started 3–4 decades ago. However, existing models are not fully equipped with the important advancements achieved in the field of data and information and computer technology (ICT). The majority of the existing models are still using the old programming languages and legacy codes, software testing is uncommon while maintenance of documentation and software/codes is also a neglected avenue. These deficiencies could be rectified through better data harmonization and interlinking of models by developing different frameworks such as BioMA (Biophysical Model Applications) and APSIM (the Agricultural Production Systems Simulator). These developments assist in data compatibility by creating a common vocabulary and datasets for model ensembling. For next-generation modeling, gaps in the existing data should be minimized, a transition from supply-driven approach to demand-driven approach is needed to develop models according to the demands of end-users. Finally, focus on the software design and development should be encouraged in the modeling community as ICT has opened new horizons in the form of parallel processing or cloud computing methods, software languages and coding standards, and the development of user-friendly community-driven mobile applications that will enable the use of models to a more divergent group of stakeholders. Overall, agricultural systems modeling needs to rapidly adopt new technologies such as ICT, big data, remote sensing, and machine learning algorithms that will help enhance crop models' accuracy and efficiency in designing sustainable agricultural systems at different farms, landscape, regional, and continental scales to meet the future demands of end-users. [ABSTRACT FROM AUTHOR]

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.

High-quality measured weather data (MWD) are essential for long-term and in-season crop model applications. When MWD is not available, one alternative for crop simulations is to employ gridded weather data (GWD), which needs to be evaluated a priori. Therefore, this study aimed to evaluate the impact of weather data from two GWD sources (NASA and XAVIER), in the way that they are available for end users, on simulating sugarcane crop performance within the APSIM-Sugar model at traditional sites where sugarcane is grown in Center-South Brazil, compared to simulations with MWD. Besides, this study also evaluated the impact of replacing GWD rainfall by the site-specific measured data on such simulations. A common sugarcane cropping system was repeatedly simulated between 1997 and 2015 for different combinations of climate input. Both NASA and XAVIER appear to be interesting for applications that only require temperature and solar radiation for predictions, such as crop phenology and potential yield. Nonetheless, GWD should be used with caution for crop model applications that rely on accurate estimation of crop water balance, canopy development, and biomass accumulation, at least with crop models that run at a daily time-step. The replacement of gridded rainfall with measured rainfall was pivotal for improving sugarcane simulations, as observed for cane yield, by increasing both agreement (NASA d index from 0.67 to 0.90; XAVIER d from 0.73 to 0.93) and R2 (NASA from 0.35 to 0.76; XAVIER from 0.43 to 0.79) and reducing root mean square errors (RMSE) from 32.8 to 16.3 t/ha when simulated with other variables of NASA data and from 27.9 to 12.7 t/ha when having XAVIER data as input. Therefore, while using both GWD sets without any correction, it is recommended to replace gridded rainfall by measured values, whenever possible, to improve sugarcane simulations in Center-South Brazil. [ABSTRACT FROM AUTHOR]

Abstract Crop productivity evaluation with models simulations can help in the prediction of harvests and in the understanding of the interactions resulting from the soil-plant-atmosphere continuum. The aim of this study was to calibrate and validate the AquaCrop model for maize crop in the edaphoclimatic conditions of Campos Gerais region, Paraná State, Brazil. The analyses were carried out for maize crop with model input data (climate, crop, soil and soil management) obtained from the ABC Foundation Experimental Station in Castro, Ponta Grossa and Socavão. The climate in the region is humid subtropical, with rainfall evenly distributed. The relief varies from flat to gently undulating. The period analyzed in the calibration and validation process comprised 2011 to 2016 and 2012 to 2016 harvests, respectively. The data used in the calibration of AquaCrop was different from those used in the validation process. Observed and simulated yields were evaluated by simple linear regression analyses, absolute and relative errors, correlation coefficient (r), concordance (d) and performance (c) indexes. The calibration of AquaCrop was satisfactory in the locations studied for maize crop, obtaining absolute errors varying from 6 to 121 kg ha–1. The highest calibration errors occurred in Castro. However, the errors were not enough to reduce the performance in the validation process for this localitie. The model validation resulted in “excellent” performance in all locations evaluated. The AquaCrop can be used to predict the maize yield with acceptable accuracy in the Campos Gerais Region, Paraná State, Brazil.

Maize yield has demonstrated significant variability both temporally and spatially. Numerous models have been presented to explain such variability in crop yield using data from multiple sources with varying temporal and spatial resolutions. Some of these models are data driven, which focus on approximating the complex relationship between explanatory variables and crop yield from massive data sets. Others are knowledge driven, which focus on integrating scientific understanding of crop growth mechanism in the modeling structure. We propose a new model that leverages the computational efficiency and prediction accuracy of data driven models and incorporates agronomic insights from knowledge driven models. Referred to as the GEM model, this model estimates three independent components of (G)enetics, (E)nvironment, and (M)anagement, the product of which is used as the predicted crop yield. The aim of this study is to produce not only accurate crop yield predictions but also insightful explanations of temporal and spatial variability with respect to weather, soil, and management variables. Computational experiments were conducted on a data set that includes maize yield, weather, soil, and management data covering 2,649 counties in the U.S. from 1980 to 2019. Results suggested that the GEM model is able to achieve a comparable prediction performance with state-of-the-art machine learning models and produce meaningful insights such as the estimated growth potential, effectiveness of management practices, and genetic progress. [ABSTRACT FROM AUTHOR]

Potato has been recognized as a crop of importance in developing countries for ensuring human nutrition and food security. The dwindling natural resources, low fertility of soils, the incidence of pests and diseases, climate change and lack of strategic planning pose major constraints for potato production in these countries. Crop models are efficient tools for assisting research and development efforts towards achieving maximum production potential in these areas as it predicts the phenology and yield of crop taking into consideration the factors affecting growth and development. A well-validated model can be used for quantification of adaptation domains, optimization and efficient utilization of resources, forecasting of pest and diseases, mitigating climate changes, yield gap analysis, a pre-harvest judgment of production and accordingly market surveillance and formulation of monitory policies. The multifaceted application of simulation models presents futuristic opportunities for developing decision supporting systems and agro-advisory services for scheduling best management practices for attaining sustained potato production. This review summarizes comprehensive information on the crop models being used along with their applications in the different aspects of potato production system towards a precision as well as smart agriculture. [ABSTRACT FROM AUTHOR]

The goals of this study are to analyse the impacts of 1.5 and 2.0°C scenarios on UK winter wheat using a combination of Global Climate Models (GCMs), crop models, planting dates and cultivars; to evaluate the impact of increased air temperature on winter wheat phenology and potential yield; to quantify the underlying uncertainties due to the multiple sources of variability introduced by climate scenarios, crop models and agronomic management. The data used to calibrate and evaluate three crop models were obtained from a field experiment with two irrigation amounts and two wheat cultivars that have different phenology and growth habit. After calibration, the model was applied to fifty locations across the wheat-growing area of the UK to cover all the main growing regions, with most points located in the main growing areas. Four GCMs, with two cultivars and five planting dates, were simulated at the end of the century. Results of this study showed that the UK potential wheat yield will increase by 2–8% under projected climate. Farmers will need to close such a gap in the future because it will have implications in terms of food security. Future climatic conditions might increase such a gap. Adaptation measures (e.g. moving the optimal planting time), along with climate-ready varieties bred for future conditions and with precision agriculture techniques can help to reduce this gap and ensure that the future actual UK wheat production will be close to the potential. [ABSTRACT FROM AUTHOR]

Biofuels are expected to play a major role in meeting California's long-term energy needs, but many factors influence the commercial viability of the various feedstock and production technology options. We developed a spatially explicit analytic framework that integrates models of plant growth, crop adoption, feedstock location, transportation logistics, economic impact, biorefinery costs and biorefinery energy use and emissions. We used this framework to assess the economic potential of hybrid poplar as a feedstock for jet fuel production in Northern California. Results suggest that the region has sufficient suitable croplands (2.3 million acres) and nonarable lands (1.5 million acres) for poplar cultivation to produce as much as 2.26 billion gallons of jet fuel annually. However, there are major obstacles to such large-scale production, including, on nonarable lands, low poplar yields and broad spatial distribution and, on croplands, competition with existing crops. We estimated the production cost of jet fuel to be $4.40 to $5.40 per gallon for poplar biomass grown on nonarable lands and $3.60 to $4.50 per gallon for biomass grown on irrigated cropland; the current market price is $2.12 per gallon. Improved poplar yields, use of supplementary feedstocks at the biorefinery and economic supports such as carbon credits could help to overcome these barriers.

Maize yield has demonstrated significant variability both temporally and spatially. Numerous models have been presented to explain such variability in crop yield using data from multiple sources with varying temporal and spatial resolutions. Some of these models are data driven, which focus on approximating the complex relationship between explanatory variables and crop yield from massive data sets. Others are knowledge driven, which focus on integrating scientific understanding of crop growth mechanism in the modeling structure. We propose a new model that leverages the computational efficiency and prediction accuracy of data driven models and incorporates agronomic insights from knowledge driven models. Referred to as the GEM model, this model estimates three independent components of (G)enetics, (E)nvironment, and (M)anagement, the product of which is used as the predicted crop yield. The aim of this study is to produce not only accurate crop yield predictions but also insightful explanations of temporal and spatial variability with respect to weather, soil, and management variables. Computational experiments were conducted on a data set that includes maize yield, weather, soil, and management data covering 2,649 counties in the U.S. from 1980 to 2019. Results suggested that the GEM model is able to achieve a comparable prediction performance with state-of-the-art machine learning models and produce meaningful insights such as the estimated growth potential, effectiveness of management practices, and genetic progress.

Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.

Abstract Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures.

Modeling of the land surface water‐, energy‐, and carbon balance provides insight into the behavior of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large‐scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyze the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS‐1D were simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in‐depth analysis of the soil SHPs and derived soil characteristics was performed to analyze why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time‐integrated behavior of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter‐comparison studies to avoid artifacts originating from the choice of PTF rather from different model structures. Plain Language Summary: Hydrological models need information about the soil physical characteristics (soil hydraulic parameters), which are in general not available if the models are applied at larger scales (region to global scale). Therefore, pedotransfer functions (PTFs) are classically used, which relate easily available soil properties such as sand‐, silt‐, clay‐content, soil organic carbon content, and soil bulk density, which are available from soil maps, to the soil hydraulic parameters. Unfortunately, there are many different PTFs available in literature. In the study presented, we analyzed the impact of different PTFs on the simulation results of water fluxes and found, that the choice of PTF impacts the simulation results. Further, some PTFs were identified as being less robust compared to others. In general, the study shows that harmonizing PTFs in model‐inter‐comparisons is needed to avoid artifacts originating from the choice of PTF rather from different model structures. Key Points: Using different PTFs in hydrological models causes substantial variability in predicted fluxesWe strongly recommend to harmonize the PTFs used in model inter‐comparison studies [ABSTRACT FROM AUTHOR]