Assessing the benefit of satellite-based Solar-Induced Chlorophyll Fluorescence in crop yield prediction

Large-scale crop yield prediction is critical for early warning of food insecurity, agricultural supply chain management, and economic market. Satellite-based Solar-Induced Chlorophyll Fluorescence (SIF) products have revealed hot spots of photosynthesis over global croplands, such as in the U.S. Midwest. However, to what extent these satellite-based SIF products can enhance the performance of crop yield prediction when benchmarking against other existing satellite data remains unclear. Here we assessed the benefits of using three satellite-based SIF products in yield prediction for maize and soybean in the U.S. Midwest: gap-filled SIF from Orbiting Carbon Observatory 2 (OCO-2), new SIF retrievals from the TROPOspheric Monitoring Instrument (TROPOMI), and the coarse-resolution SIF retrievals from the Global Ozone Monitoring Experiment-2 (GOME-2). The yield prediction performances of using SIF data were benchmarked with those using satellite-based vegetation indices (VIs), including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), and land surface temperature (LST). Five machine-learning algorithms were used to build yield prediction models with both remote-sensing-only and climate-remote-sensing-combined variables. We found that high-resolution SIF products from OCO-2 and TROPOMI outperformed coarse-resolution GOME-2 SIF product in crop yield prediction. Using high-resolution SIF products gave the best forward predictions for both maize and soybean yields in 2018, indicating the great potential of using satellite-based high-resolution SIF products for crop yield prediction. However, using currently available high-resolution SIF products did not guarantee consistently better yield prediction performances than using other satellite-based remote sensing variables in all the evaluated cases. The relative performances of using different remote sensing variables in yield prediction depended on crop types (maize or soybean), out-of-sample testing methods (five-fold-cross-validation or forward), and record length of training data. We also found that using NIRv could generally lead to better yield prediction performance than using NDVI, EVI, or LST, and using NIRv could achieve similar or even better yield prediction performance than using OCO-2 or TROPOMI SIF products. We concluded that satellite-based SIF products could be beneficial in crop yield prediction with more high-resolution and good-quality SIF products accumulated in the future

Modeling high-resolution climate change impacts on wheat and maize in Italy

Abstract The Mediterranean basin has been identified as a prominent hotspot of climate change, with expected negative impacts on crop productivity, among others. Given the primary role that agriculture has to sustain cultural values, economic opportunities, and food security, it is crucial to identify specific risks in agriculture due to climate change, which can address more effective adaptation strategies and policies to cope with climate change. This study aims to evaluate the high-resolution impacts of climate change on the length of the growing cycle and yield of durum wheat, common wheat, and maize in Italy by using the CERES-Wheat and CERES-Maize crop models implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software. A digital platform (GIS-DSSAT) was developed to couple crop simulation models with dynamically downscaled climate projections at high resolution for Italy, which can better represent the Italian landscape complexity and the spatial distribution of different pedological and crop management features, providing more detailed information on the expected impacts on crops respect to previous studies at a coarser resolution. The projections have been extended for two climate change scenarios and accounting for uncertainty, either considering or not the potential direct effects of increasing atmospheric CO2 concentrations ([CO2]). Results show that climate change may affect Italian cereal production in the medium to long term periods. Maize is the main affected crop, with yield reductions homogeneously distributed from North to South Italy. Wheat yield is expected to decrease mainly in southern Italy, while northern Italy may benefit from higher precipitation regimes. Higher levels of atmospheric CO2 concentrations may partially offset the negative impact posed by climate change and increase the benefits in the northern regions, especially for common and durum wheat

Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs

Gridded model assessments require at least one climatic and one soil database for carrying out the simulations. There are several parallel soil and climate database development projects that provide sufficient, albeit considerably different, observation based input data for crop model based impact studies. The input database related uncertainty of the Biome-BGCMuSo agro-environmental model outputs was investigated using three and four different gridded climatic and soil databases, respectively covering an area of nearly 100.000 km2 with 1104 grid cells. Spatial, temporal, climate and soil database selection related variances were calculated and compared for four model outputs obtained from 30-year-long simulations. The choice of the input database introduced model output variability that was comparable to the variability the year-to-year change of the weather or the spatial heterogeneity of the soil causes. Input database selection could be a decisive factor in carbon sequestration related studies as the soil carbon stock change estimates may either suggest that the simulated ecosystem is a carbon sink or to the contrary a carbon source on the long run. Careful evaluation of the input database quality seems to be an inevitable and highly relevant step towards more realistic plant production and carbon balance simulations

A multi-scale and multi-model gridded framework for forecasting crop production, risk analysis, and climate change impact studies

Regional crop production forecasting is growing in importance in both, the public and private sectors to ensure food security, optimize agricultural management practices and use of resources, and anticipate market fluctuations. Thus, a model and data driven, easy-to-use forecasting and a risk assessment system can be an essential tool for end-users at different levels. This paper provides an overview of the approaches, algorithms, design, and capabilities of the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) for gridded crop modeling and yield forecasting along with risk analysis and climate impact studies. CRAFT is a flexible and adaptable software platform designed with a user-friendly interface to produce multiple simulation scenarios, maps, and interactive visualizations using a crop engine that can run the pre-installed crop models DSSAT, APSIM, and SARRA-H, in concert with the Climate Predictability Tool (CPT) for seasonal climate forecasts. Its integrated and modular design allows for easy adaptation of the system to different regional and scientific domains. CRAFT requires gridded input data to run the crop simulations on spatial scales of 5 and 30 arc-minutes. Case studies for South Asia for two crops, including wheat and rice, shows its potential application for risk assessment and in-season yield forecasting

Evaluating remotely piloted aircraft estimates of crop height and LAI against satellite and crop model outputs

Crop simulation models (CSM) have been a method for decision makers to study the effects of crop management activities for predicting, planning, and improving crop growth for the past several decades. While the applicability and robustness of CSMs had been rapidly evolving, the methods of gathering input and validation data for CSMs has remained predominantly the same. However, the application of remote sensing technologies including remotely piloted aircraft systems (RPAS) and satellites for agricultural purposes has demonstrated the potential for automated rapid and high detail CSM validation data. This study evaluated the accuracy of validation data acquired using RPAS and satellite technologies when compared to CSM outputs and observed crop measurements. Imagery of an agricultural field was acquired throughout a growing season with the use of a multi-sensor RPAS and existing satellite missions. Field work was performed alongside the RPAS imagery acquisitions to collect input data for crop modelling and accuracy assessments. Using the acquired imagery, the crop height and leaf area index (LAI) values of crops in the field were estimated for multiple dates. The LAI was estimated using 1) a regression-based method and 2) a function of the fractional vegetation cover and the leaf angle distribution method. A CSM was run alongside the remote sensing to simulate crop height and LAI values. When the estimated values were compared to observed measurements, showing the RPAS-derived crop height values were significantly more accurate (RMSE=193.6 cm, RMSE=161.3 cm) than the satellite-derived crop heights values (RMSE=223.4 m, RMSE=117.1 m respectively) yet less accurate than the CSM crop heights values. The RPAS-derived LAI value accuracies (RMSE=0.42, RMSE=0.66) and satellite-derived LAI value accuracies (RMSE=0.56, RMSE=0.56) were similar but the RPAS was found to, on average, estimate LAI more accurately than the CSM. Overall, the RPAS methods showed moderate accuracy across both crop height and LAI estimations and was found to perform better than the CSM in some situations. Future work may include additional imagery acquisitions throughout a growing season to further test the accuracies of RPAS-derived estimates as well as integrating estimates directly into CSMs for validation purposes

Improving the drought risk assessment and preparedness for winter wheat farming in Oklahoma

Droughts pose a persistent threat to agriculture in the Southern Great Plains (SGP). Oklahoma is a major contributor to dryland winter wheat farming in the SGP, acrop that is highly susceptible to drought episodes. Modern tools of environmental monitoring and crop simulations provide great opportunity to improve agricultural drought risk assessment and preparedness. However, in the wheat belt of Oklahoma, these modern technologies have been less utilized to understand the dynamics of droughtand its effects on winter wheat growth. There is an immediate need to investigate the prospective of advanced environmental monitoring networks and practitioner-oriented crop models to mitigate the impacts of dry periods on crop yield. The objectives of this study were to: 1) develop a new drought index using soil moisture and weather data for improved drought monitoring of winter wheat; 2) calibrate and validate a crop model and employ it to study the impacts of planting date and water availability at planting on theyield of dryland and irrigated winter wheat; and 3) apply the calibrated crop model across the winter wheat belt in Oklahoma to investigate the spatial variation in yield and itsdrought sensitivity. The development of a new drought index showed that soil moisture information in conjunction with reference evapotranspiration can improve the estimation of drought magnitude. Also, the new drought index correlated well with the winter wheat yield, showing its potential for agricultural drought monitoring for Oklahoma. Long-term crop modeling study for winter wheat in Oklahoma revealed that October planting dates usually provide better yields in comparison to September sowing. Moreover, the considerable impact of soil moisture at the time of sowing was noted on overall wheat yields, and the irrigation had noticeable positive effect on yield, especially in drought years. Gridded crop modeling helped understanding the spatial variation in potential dryland yields in wheat belt of Oklahoma. Furthermore, it was found that the winter wheat yield was highly correlated to drought in the months of March to May, and West Central climate division was highly sensitive to dry periods in Oklahoma