Crop Simulation Models Research Articles

Drought response of water-conserving and non-conserving spring barley cultivars.

Breeding barley cultivars adapted to drought requires in-depth knowledge on physiological drought responses. We used a high-throughput functional phenotyping platform to examine the response of four high-yielding European spring barley cultivars to a standardized drought treatment imposed around flowering. Cv. Chanell showed a non-conserving water-use behavior with high transpiration and maximum productivity under well-watered conditions but rapid transpiration decrease under drought. The poor recovery upon re-irrigation translated to large yield losses. Cv. Baronesse showed the most water-conserving behavior, with the lowest pre-drought transpiration and the most gradual transpiration reduction under drought. Its good recovery (resilience) prevented large yield losses. Cv. Formula was less conserving than cv. Baronesse and produced low yet stable yields. Cv. RGT's dynamic water use with high transpiration under ample water supply and moderate transpiration decrease under drought combined with high resilience secured the highest and most stable yields. Such a dynamic water-use behavior combined with higher drought resilience and favorable root traits could potentially create an ideotype for intermediate drought. Prospective studies will examine these results in field experiments and will use the newly gained understanding on water use in barley to improve process descriptions in crop simulation models to support crop model-aided ideotype design.

Does drought stress eliminate the benefit of elevated CO2 on soybean yield? Using an improved model to link crop and soil water relations

Crop simulation models are indispensable tools that facilitate studies to assess climate impacts and adaptation responses, but have not been adequately tested in terms of accurately predicting crop growth and water stress responses to high carbon dioxide concentrations [CO2]. The soybean model, GLYCIM, previously modified with a coupled leaf-level gas exchange – energy balance model, was integrated with a two-dimensional convective-diffusive root growth module which linked soil and leaf water potentials with the regulation of stomatal conductance. We evaluated the accuracy of this modified GLYCIM using experimental data from a Free-Air CO2 Enrichment (FACE) site, SoyFACE, and used the calibrated model to quantify current and projected elevated CO2 effects on soybean yields in the United States (US) Midwest. The model accurately matched SoyFACE observed data including canopy temperature (RMSE, 2.26 -2.42 °C; IA, 0.89-0.90), photosynthesis rate (RMSE, 6.33 -12.88 µmol m−2 s−1; IA, 0.82-0.92), seasonal LAI (RMSE, 0.96 -1.08 cm2 cm−2; IA, 0.91-0.93), biomass (RMSE, 2.06 -2.34 Mg ha−1; IA, 0.87-0.90), soil water contents (RMSE, 5.42 -7.82 %; IA, 0.80-0.85) and root length (RMSE, 2.12 -5.64 m plant−1; IA, 0.70-0.86). GLYCIM also reproduced the observed declining response of yield to CO2 fertilization with increasing drought. Using an ensemble of five climate model projections, GLYCIM showed that the CO2 fertilization effect on yield was highest in northern counties and declined in southern and the western counties by late 21st century in response to increased drought characterized by higher daily maximum temperature and decreased rainfall. This study highlights the need to continue to test and improve crop models and use geospatially referenced adaptive strategies to capitalize the benefits from elevated [CO2] to better counteract increasing drought.

Crop Yield Assessment Using Field-Based Data and Crop Models at the Village Level: A Case Study on a Homogeneous Rice Area in Telangana, India

Crop yield estimation has gained importance due to its vital significance for policymakers and decision-makers in enacting schemes, ensuring food security, and assessing crop insurance losses due to biotic and abiotic stress. This research focused on rice yield estimation at the field level in the Karimnagar district of Telangana during 2021 and 2022 by employing the leaf area index (LAI) as the primary criterion for integrating remote sensing technology and crop simulation models. Using Sentinel-2 satellite data, the rice crop was mapped with the help of ground data and machine learning algorithms, attaining an accuracy of 93.04%. Crop management data for the DSSAT tool were collected during the field visits; the model results revealed a 0.80 correlation between observed and predicted yields. Due to its strong correlation with LAI (0.82), the normalized difference vegetation index (NDVI) was selected as the critical element for integration with the model. A spatial LAI map was generated using the linear equation developed between the NDVI and LAI. The relationship between LAI and yield was used to create a spatial yield map. The study’s findings show that assimilating remote sensing data with crop models enhances the precision of rice yield prediction for insurance companies and policy- and decision-makers.

The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.

Downscaling the APSIM crop model for simulation at the within-field scale

CONTEXTMost crop models are designed for point-based modeling and to simulate agronomic variables on their native spatial footprint, i.e. typically as a uniform field-scale value. Precision agriculture needs crop model simulations at sub-field scales to support differential management application. Spatialization processes are used to change the simulation scale of crop models. OBJECTIVEThe objective of this study is to investigate the spatialization of a complex crop model by using a spatial calibration approach to modify its native spatial footprint and to evaluate if it is relevant to use this kind of crop model at the within-field scale. METHODSAPSIM was spatialized to simulate durum wheat yield at different spatial scales (field, within-field and site-scale) on an experimental field under Mediterranean conditions in southern Italy. Ancillary soil data were used to derive potential management (modeling) zones at different scales, which were then used to spatially calibrate soil and biomass parameters in APSIM to spatially predict yield in two different production years (one year was used for calibration and the other for evaluation). Spatialized crop model performances were evaluated using the spatial balanced accuracy (SBA) score, a metric to evaluate the global preservation of patterns between maps. RESULTS AND CONCLUSIONSThe spatial structure of the yield data influenced the effectiveness of the spatial calibration process. When the agronomic variable (durum wheat yield) was spatially structured, a spatialized APSIM approached performed best (5-zone modeling scale, SBA = 0.17) and outperformed the field-scale (native footprint) model (SBA = 0.19). In contrast, when the target agronomic variable was more random (less spatially structured), the uniform field-scale modeling performed best and spatial calibration had no benefit. The spatialized APSIM performances were mainly based on the reliability of the delineated zones that undeniably affected the quality of the spatialized model outputs. Thus, more research is needed on how best to model scale-dependent processes to have more reliable modeling at the within-field scale. SIGNIFICANCEBased on the example of a complex crop model like APSIM, this study showed that spatial calibration can be effective and has a role to play in the spatialization of complex crop models.

OFF-SEASON MAIZE YIELD FORECASTING FOR DIFFERENT SOWING DATES

Brazil is the world's third-largest maize producer. Its yield is influenced by climate, soil conditions, management and their interactions. Identifying the most suitable sowing window and using yield forecasting systems allows for increased yields and better harvest management. Crop simulation models can be used to assess crop responses to various conditions. This study aimed to identify the most favorable planting date for off-season maize by using the DSSAT CSM-CERES-Maize model for Jataí, Goiás state. Using meteorological data from 1986 to 2015, eight productivity forecasting strategies were tested. The results of the model application at different sowing dates indicated that the dates in January tended to present a more favorable attainable yield (Ya), i.e., values closer to the potential yield (Yp) of the crop, whereas in February, Ya were affected by lower precipitation from April to June. The best date for maize sowing was January 25th. The simulations indicated the possibility of predicting the off-season maize productivity in Jataí with high precision and accuracy up to 30 days prior to harvest (R² ≥ 0.81, d ≥ 0.90, and c ≥ 0.81).

Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management variables

The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.

Onion (Allium cepa L.) Production Guide Simulation Program using C Programming

In addition to environmental factors, the success of onion cultivation also depends on adopting sophisticated agricultural techniques and methods. Crop simulation models are now required to enhance onion cultivation, reduce risks, and increase yields. This study aimed to develop and evaluate a rudimentary C programming language-based onion production simulation software. This research created a simulation of a guide for producing onions. This simulation tool integrates agricultural expertise with technology to provide onion producers, researchers, and agricultural stakeholders with valuable insights and guidance. The simulation assists users in optimizing onion cultivation from planting to harvest through an in-depth look at best practices. The simulation incorporates essential facets of onion cultivation, such as planting techniques, irrigation strategies, insect and disease management, and market-driven decision-making. Advantages of the simulation include precise financial planning, resource allocation, and risk management. Users gain a greater comprehension of expected income, allowing them to budget more effectively and improve their market access strategies. In addition, the simulation encourages adopting sustainable agricultural practices, technological integration, and innovation in response to onion producers' evolving challenges.

Advancements in remote sensing based crop yield modelling in India

Crop yield prediction at regional levels is an essential task for the decision-makers for rapid decision making. Pre-harvest prediction of a crop yield can prevent a disastrous situation and help decision-makers to apply more reliable and accurate strategies regarding food security. With the advent in digital world, various advanced techniques are employed for crop yield prediction. Remote Sensing (RS) data with its capability to provide the synoptic view of the Earth’s surface, has numerous returns in the area of crop monitoring and yield prediction. This study provides as a review for the advanced techniques for crop yield prediction in India with RS data as a base. The advanced techniques like RS based statistical yield modelling, machine learning based yield modelling, semi-physical yield modelling are described in the current study. The assessment of the studies related to integration of RS data in crop simulation model is also described in a section. All the techniques involved in the current study show significant improvements in crop yield prediction, enabling the development of new agricultural applications in India.

Evaluation of Gridded Meteorological Data for Crop Sensitivity Assessment to Temperature Changes: An Application with CERES-Wheat in the Mediterranean Basin

In areas with a limited or non-existent network of observing stations, it is critical to assess the applicability of gridded datasets. This study examined the agreement of Agri4Cast and E-OBS at two spatial resolutions (10 km (EOBS-0.1) and 25 km (EOBS-0.25)) in 13 Mediterranean stations nearby to wheat crops and how this agreement may influence simulated potential development and production with the crop simulation model (CSM) CERES-Wheat in historical and near-future (2021–2040) (NF) periods. A wide range of sensitivity tests for maximum and minimum air temperatures and impact response surfaces were used for the future projections. EOBS-0.1 showed the lowest discrepancies over observations. It underestimated statistical measures of temperature and precipitation raw data and their corresponding extreme indices and overestimated solar radiation. These discrepancies caused small delays (5–6 days, on average) in crop development and overestimations (8%) in grain production in the reference period. In the NF, the use of EOBS-0.1 reduced by a few (2–3) days the biases in crop development, while yield responses differed among stations. This research demonstrated the ability of EOBS-0.1 for agricultural applications that depend on potential wheat development and productivity in historical and future climate conditions expected in the Mediterranean basin.

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively.

Adapting the CROPGRO Perennial Forage model to predict growth and development of Pensacola bahiagrass

Bahiagrass (Paspalum notatum Flüggé) – a warm-season perennial grass – is one of the most important forage species for livestock production in the southeastern United States. Bahiagrass pastures are often managed extensively, without the data and tools necessary for complex management. Crop simulation models have been previously developed for warm-season grasses, enabling forecast of physiological variables and evaluation of various management scenarios. The objective of this study is to calibrate the CROPGRO Perennial Forage model (CROPGRO-PFM) for bahiagrass and to evaluate the performance of the new model across six experimental datasets. Previously developed parameters for the palisadegrass (Brachiaria brizantha cv. Xaraes) model were used initially, followed by modification of parameters more predictive of bahiagrass and its differences from palisadegrass. Major changes included calibration of plant composition, cool season temperature sensitivity, and photoperiod-induced dormancy parameters, corresponding to previous observations of bahiagrass physiology. The final adapted model resulted in effective predictive accuracy for important state variables – harvested herbage mass (RMSE = 729 kg ha−1, d-index = 0.85), above-ground biomass (RMSE = 729 kg ha−1, d-index = 0.82) and crude protein concentration (RMSE = 3.1%, d-index = 0.71). Given the wide range of experimental conditions used for model development, these results suggest that the calibrated CROPGRO-PFM model will be effective for simulating physiological variables across bahiagrass pastoral systems.

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments.

Evaluation of DSSAT-CROPGRO Module for Spatial Yield Estimation of Chickpea in Vidisha (Madhya Pradesh) and Nagaur (Rajasthan), India

Crop simulation models are often used to characterize, develop and assess field crop production practices. The present study was carried out for chickpea spatial yield estimation at Vidisha of Madhya Pradesh and Nagaur district of Rajasthan employing the DSSAT model. In this study, the DSSAT-CROPGRO module was used to estimate chickpea yield during rabi 2022. To simulate the yield, DSSAT required datasets of crop growth and management, daily weather data, and soil data were provided. The simulated yield was validated using the observed yield through CCEs from farmers’ fields. When the observed and simulated yields were compared, their deviation was found to be less than 20 percent for all varieties at experimental locations of the Vidisha and Nagaur districts. The observed yield of Chickpea matched well after calibration which showed that model could simulate the yield with high accuracy as it showed R2, d, and MAPE of 0.87, 0.92, and 7.30 for calibration and 0.88, 0.90, and 7.60 for the validation, respectively. The model has been successfully calibrated and validated for the chickpea at spatial level and it can be taken for further applications in natural resources management and climate change impact studies.

Coupling crop simulation modelling and multi-criteria decision aid for ranking the sustainability of cropping sequences

IntroductionCrop sequences are important to make efficient use of natural resources and ensure food security. Climate change affects the sustainability of crops in cropping sequence. The application of crop sequence modelling to evaluate the sustainability of cropping sequences under projected climate is a less explored area.MethodsThe present work evaluated the impact of projected climate on six cropping sequences viz., rice-mustard-groundnut, rice-lentil-groundnut, rice-potato-groundnut, rice-wheat-groundnut, rice-maize-groundnut and rice-mustard-fallow in the Lower Gangetic Plain of India. CMIP-5 multi-model ensemble, long-term crop sequence modelling using DSSAT and a multiple criteria decision analysis tool, TOPSIS was used to evaluate sustainability. Future climate scenarios were developed using 29 GCMs from which a subset of 5 representative GCMs was selected for mid-century (2040-2069) and end-century (2070-99) under RCP4.5 and RCP8.5 emission scenarios. Weighted average ensemble yield, ETa and nitrogen fixed by all cropping sequences for the study period were used to rank cropping sequences for sustainability using TOPSIS.Results and DiscussionThe minimum and maximum temperatures during mid and end-centuries were projected to be consistently higher than the baseline period (1980-2010) for all the cropping seasons. Under all the periods, rice-lentil-groundnut had the highest weighted average ensemble yield, followed by rice-wheat-groundnut. Rice-lentil-groundnut fixed the highest quantity of nitrogen, followed by rice-maize-groundnut. Ranking of cropping sequences for sustainability by the TOPSIS method indicated that during mid-century (under both RCP4.5 and RCP8.5), the rice-lentil-groundnut sequence will be the most sustainable cropping sequence. However, by the end century, the rice-wheat-groundnut sequence will be the most sustainable cropping sequence, followed by rice-lentil-groundnut under both RCP4.5 and 8.5. In all the cropping sequences, rice will be benefitted by higher amount of nitrogen fixed and preceding groundnut. The three parameters considered for defining sustainability in crop production (yield, ETa and N-fixed) ensures higher produce and return for the farmer, less dependence on irrigation sources and increase in soil nitrogen content. In this paper, we show for the first time that cropping sequences can be evaluated for sustainability by combining crop sequence modelling, GCM ensemble and multi-criteria decision analysis. The results of the study will help the farmers of the study area to opt for the most sustainable cropping sequence and other alternatives in the context of climate change.

Development of a cotton fiber quality simulation module and its incorporation into cotton crop growth and development model: GOSSYM

GOSSYM is a mechanistic, process-level cotton crop simulation model. It can simulate the growth and development of cotton under different environmental and management conditions and provide information on crop growth and development, various stresses, and yield. The current model version does not have the capability of simulating cotton fiber quality. Cotton fiber quality is as significant as fiber quantity. In this study, a cotton fiber quality simulation module is developed and integrated into GOSSYM. The functional relationships between the fiber quality and the major factors influencing fiber quality (temperature, water, and nutrient status) are established based on the temperature, water, and nutrient-controlled sunlit soil plant atmospheric research (SPAR) chamber experiments. In the developed model, the potential fiber quality in terms of four major fiber quality indices (fiber length, fiber strength, micronaire, and uniformity) is estimated based on the temperature function, and actual fiber quality indices are determined by reducing potential quality as a function of water and nutrient status in the crop. The newly developed model's fiber quality modeling capabilities are demonstrated using illustrative examples from a case study. Illustrated examples analyze the effect of different temperatures, nitrogen fertilizer application rates, irrigation, planting date, and atmospheric carbon dioxide concentrations on fiber quality indices. Simulation results are discussed by correlating them with underlying processes (growth and development, water, and nitrogen status), literature data, and functional relationships incorporated into the fiber quality module in GOSSYM. The model has demonstrated a reasonable simulation of fiber quality. The developed model can be a valuable tool for accurate cotton fiber quality simulations, lint yield production, fiber quality optimization, and making policy decisions under varying environmental and management conditions.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

Introduction: The impact of climate change on food production in South Africa is likely to increase due to low rainfall and frequent droughts, resulting in food insecurity in the future. The use of well-calibrated and validated crop models with climate change data is important for assessing climate change impacts and developing adaptation strategies. In this study, the decision support system for agrotechnology transfer (DSSAT) crop model was used to predict yield using observed and projected climate data.Materials and Methods: Climate, soil, and crop management data were collected from wheat-growing study sites in Bethlehem, South Africa. The DSSAT wheat model (CROPSIM-CERES) used was already calibrated, and validated by Serage et al. (Evaluating Climate Change Adaptation Strategies for Disaster Risk Management: Case Study for Bethlehem Wheat Farmers, South Africa, 2017) using three wheat cultivar coefficients obtained from the cultivar adaptation experiment by the ARC-Small Grain Institute. The model was run with historical climate data for the eastern Free State (Bethlehem) from 1999 to 2018 as the baseline period. To determine the effects of climate change, the crop model simulation for wheat was run with future projections from four Global Climate Models (GCM): BCC-CSM1_1, GFDL-ESM2G, ENSEMBLE, and MIROC from 2020 to 2077.Results: The average wheat yield for the historic climate data was 1145.2 kg/ha and was slightly lower than the highest average yield of 1215.9 kg/ha from GCM ENSEMBLE during Representative concentration pathways (RCP) 2.6, while the lowest yield of 29.8 kg/ha was produced during RCP 8.5 (GCM GFDL-ESM2G). Model GFDL-ESM2G produced low yields (29.8–47.74 kg/ha) during RCP 8.5 and RCP 6.0, respectively. The yield range for GCM BCC-CSM1_1 was 770.2 kg/ha during RCP 2.6 to 921.68 kg/ha during RCP 4.5 and 547.84 kg/ha during RCP 8.5 to 700.22 kg/ha during RCP 2.6 for GCM MIROC.Conclusion: This study showed a declining trend in yield for future climate projections from RCP2.6 to RCP8.5, indicating that the possible impacts of higher temperatures and reduced rainfall in the projected future climate will slightly decrease wheat production in the eastern Free State. Adaptation measures to mitigate the potential impact of climate change could include possible changes in planting dates and cultivars. Using a crop model to simulate the response of crops to variations in weather conditions can be useful to generate advisories for farmers to prevent low yield.

CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation

Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales.

Enhancing the yield of rice and corn using field-specific crop production management recommendations in selected areas in the Province of Iloilo, Philippines

The researcher aims to determine the best planting time based on Smarter Approaches to Reinvigorate Agriculture as an Industry (SARAI) and the Crop Simulation Model (Planting Calendar), and formulate site-specific nutrient management (SSNM). The research study was conducted from May 2018 to October 2021. There were twelve (12) adaptive planting calendar field trials in six (6) sites for Rice and six sites for Corn. The studies were conducted in farmers’ fields with two treatment plots measuring (15m2 x 15m2) 225 square meters each plot to compare the yield of Rice and Corn using Farmers Practice, and SARAI recommended planting date. On the other hand, Nutrient Management for corn trials was conducted in two (2) municipalities: Lambunao and Alimodian, with five (5) sites per municipality for Lambunao, Iloilo, and five (5) locations for Alimodian, Iloilo. A 225 square meter area treatment plots were used to compare yield of corn using Farmer’s Fertilizer Management, Site Specific Nutrient Management Practice and unfertilized plot as Check Plots treatment. Results of the trials revealed that the yields of rice and corn following SARAI recommendations regarding planting dates were highly significant compared to farmers practice. Site-specific nutrient management recommendations for corn also showed a significant increase in terms of kernel yield.

Wheat crop traits conferring high yield potential may also improve yield stability under climate change

Abstract Increasing genetic wheat yield potential is considered by many as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulation model solution in the SIMPLACE framework to explore yield sensitivity to select trait characteristics (radiation use efficiency [RUE], fruiting efficiency and light extinction coefficient) across 34 locations representing the world’s wheat-producing environments, determining their relationship to increasing yields, yield variability and cultivar performance. The magnitude of the yield increase was trait-dependent and differed between irrigated and rainfed environments. RUE had the most prominent marginal effect on yield, which increased by about 45 % and 33 % in irrigated and rainfed sites, respectively, between the minimum and maximum value of the trait. Altered values of light extinction coefficient had the least effect on yield levels. Higher yields from improved traits were generally associated with increased inter-annual yield variability (measured by standard deviation), but the relative yield variability (as coefficient of variation) remained largely unchanged between base and improved genotypes. This was true under both current and future climate scenarios. In this context, our study suggests higher wheat yields from these traits would not increase climate risk for farmers and the adoption of cultivars with these traits would not be associated with increased yield variability.