World Food Studies Research Articles

Accuracy and robustness of a plant-level cabbage yield prediction system generated by assimilating UAV-based remote sensing data into a crop simulation model

AbstractIn-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

Responses of potential double cropping areas expansion and appropriate crop management practices to climate change in northern China

IntroductionGlobal climate change has led to increases in the temperature and decreases in the number of frost days in northern China, facilitating a shift from a single cropping system (SCS, spring maize) to a double cropping system (DCS, winter wheat-summer maize rotation).MethodsTherefore, under the current climate conditions, DCS expansion should be evaluated, and new planting schemes should be explored. In this paper, we identified the areas with potential for DCS in northern China considering an annual accumulated temperature of >0°C. The World Food Studies simulation model was used to simulate the yield, irrigation requirement (IR), and net income under various crop management conditions when considering the maximum yield and water use efficiency (WUE) of crops.ResultsOur results indicated that the potential DCS area increased by approximately 31.51 × 104 km2 in northern China, with the primary DCS areas being located in the provinces of Gansu, Shaanxi, Shanxi, Hebei, and Liaoning. Regarding variety selection, winter wheat and summer maize varieties with early and mid-early maturation were found to be favored for the potential DCS areas. The sowing dates corresponding to the maximum WUEs of the crops were later than those corresponding to the maximum yields. In the potential DCS areas, under the maximum yield condition, the average unit total yield, IR, and net income increased to 2700 kg ha−1, 305 mm, and 607 USD ha−1, respectively, whereas under the maximum WUE condition, increases of 2862 kg ha−1, 284 mm, and 608 USD ha−1, respectively, were observed. The average unit total yield of the DCS was 15927 and 13793 kg ha−1 under the maximum yield and maximum WUE condition, respectively.DiscussionOur findings may clarify the effects of climate change on agricultural production patterns and indicate suitable crop management practices.

A study on parameter calibration of a general crop growth model considering non-foliar green organs

Accurate crop yield estimation is essential for ensuring national food security and sustainable development. The crop growth model is one of the primary methods for estimating production and has been effectively applied in simulating crop growth and development, environmental factor effects and yield formation processes. However, many related studies have focused on Gramineae crops, such as wheat, rice and maize, while there has been little research on rape and soybean yield estimation. Non-foliar green organs such as rape siliques and soybean pods make vital contributions to plant photosynthesis and influence crop output. The parameter calibration method based on the leaf area index (LAI) cannot satisfy the existing demand for high-precision yield estimation for crops with active non-foliar green organs. Therefore, a new method for crop growth model calibration was proposed, which considers non-foliar green organs and plant photosynthetic succession processes and combines them with those of leaves to construct a photosynthetic area index (PAI). With wheat, rape and soybean as the research objects, their yields were simulated via the proposed crop growth model calibration method. The results showed that using the proposed calibration method to calibrate World Food Study (WOFOST) model parameters on the basis of the PAI improved the yield estimation accuracy over that of the crop model calibration method based on the LAI. The determination coefficient (R2) of the total dry weight of storage organs (TWSO) simulation value increased from 0.73 (using the LAI) to more than 0.90 (using the PAI). The R2 values of TWSO at the rape calibration and verification points were 0.910 and 0.922, respectively, and the R2 values of TWSO at the soybean calibration and verification points were 0.741 and 0.926, respectively. The above results verified the feasibility and effectiveness of the proposed calibration method. Consequently, the application of the crop model calibration method proposed in this paper is important for accurately estimating the crop yield of plants with active non-foliar green organs, promoting the expansion of general crop models for different types of crops and achieving high-precision crop yield estimations.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Improving maize yield estimation by assimilating UAV-based LAI into WOFOST model

ContextCrop growth models were widely applied for simulating the dynamic growth of crops at multi-scales. The data assimilation by integrating the remote sensing data retrieved crop parameters and crop models have showed great potentials for describing the crop growth and assessing the agricultural yields. ObjectiveThe purpose of this study was to integrates sequential observations of crop phenotyping traits from Unmanned Aerial Vehicles (UAV) remote sensing into World Food Studies (WOFOST) model to improve the simulation of crop growth processes. MethodsTwo years of Leaf Area Index (LAI) of summer maize retrieved from Unmanned Aerial Vehicles (UAV)-RGB images was assimilated into the WOFOST using the Ensemble Kalman Filter (EnKF). The sensitive crop parameters of WOFOST were firstly identified using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, and then the parameters were adjusted and confirmed using the SUBPLEX optimization algorithm. The LAI data was assimilated into WOFOST model using EnKF by minimizing the differences between the UAV-retrieved LAI and crop-simulated LAI. Results indicated assimilating LAI into WOFOST model significantly improved the accuracy of maize yield prediction. ResultsCompared with non-assimilation, data assimilation reduced the Root Mean Square Error (RMSE) from 413 to 132 kg/ha for 2020, and from 392 to 215 kg/ha for 2021, respectively. Through the effects of different ensemble size and different time-point for data assimilation, it was obtained that the accuracy of yield prediction achieved the highest when ensemble size was 100 at reproductive growth stage. ConclusionsIntegrating UAV-based crop traits into WOFOST model using data assimilation (EnKF) could effectively improve the maize yield accuracy.

The adaptability and irrigation constraints analysis of the WOFOST model for grain production in the Songhua River Basin.

The Songhua River Basin, a vital grain-producing area in China, faces challenges due to the uneven distribution of water resources and the intensive water demands of agriculture. To enhance agricultural development and effectively manage water scarcity, it is essential to identify the water-saving potential of major staple crops - corn, wheat, and rice. This study enhances the World Food Studies (WOFOST) model by refining the day of year for the developmental vegetative stage (DVS), thereby improving the representation of phenological stages for spring maize, spring wheat, and rice within the model. This refinement offers a detailed analysis of the potential and rainfed yields. The results from the modified WOFOST model show promising simulation outcomes for the biomass and yield of maize, wheat, and rice, with Nash-Sutcliffe efficiency (NS) and index of agreement (IoA) values all exceeding 0.7. An analysis of photothermal potential yields (Yp) and rainfed yields (Yr) revealed minimal differences in yields for spring maize and rice across various rainfall frequencies. Specifically, the average photothermal utilization rates (LTs) are 93.57% for maize and 85.25% for rice. In contrast, the rainfed yield for wheat is lower than its photothermal yield, with an LT of 43.66%. These findings suggest that in the Songhua River Basin, maize and rice offer greater potential for water conservation compared to wheat. It is recommended to judiciously reduce irrigation during the growing seasons of spring maize and rice to help alleviate agricultural water use pressures. © 2024 Society of Chemical Industry.

WOFOST-N: An improved WOFOST model with nitrogen module for simulation of Korla Fragrant pear tree growth and nitrogen dynamics

Few studies have focused on simulating fruit tree growth under fertilization constraints and evaluating nitrogen utilization efficiency. This study aimed to improve the WOFOST (World Food Studies) model by embedding the nitrogen dynamics module into the original WOFOST model (WOFOST-N) to simulate the Korla Fragrant pear tree growth, nitrogen transport, nitrogen use efficiency and nitrogen stress. In addition, we used the BBCH scale to provide a more detailed description of the phenological development process of pear trees to improve phenology simulation performance. Field trials were conducted in 2021 and 2022 in two pear orchards in the Alar region of China. The results showed that the simulated total dry weight (leaves and fruits, TAGPl,f) and leaf area index (LAI) growth dynamics agreed well with the measured values in each treatment in 2021 and 2022. The R2 values of the simulated TAGPl,f was between 0.87 and 0.97, and the R2 values of the simulated LAI were between 0.80 and 0.95. The improved model also showed relatively high simulation accuracy (8.2 % ≤ NRMSE ≤ 20.5 % for TAGPl,f, 7.1 % ≤ NRMSE ≤ 20.1 % for LAI). The improved model had good global simulation performance for soil nitrogen content in six treatments with a good consistency (R2 = 0.87 in 2021 and 0.89 in 2022) and low error (NRMSE = 12.4 % in 2021 and 11.8 in 2022). The simulated yields and nitrogen use efficiencies (NUE) in the two orchards also showed good performance, with R2 = 0.94 and 0.93, NRMSE = 5.2 % and 7.5 % for yields, R2 = 0.93 and 0.96, NRMSE = 10.7 % and 9.6 % for NUE, respectively. The proposed method provides a promising method to simulate fruit trees' growth and nitrogen dynamic effects.

Based on historical weather data to predict summer field-scale maize yield: Assimilation of remote sensing data to WOFOST model by ensemble Kalman filter algorithm

Effective support for agricultural production-management strategies relies on the accurate monitoring of crop growth and grain yield estimation at the field scale, making it an urgent need for the development of precision agriculture. Crop models are capable of simulating the growth and development of crops. In addition, Unmanned Aerial Vehicle (UAV) provides high temporal and spatial resolution for remote sensing, which can quickly and accurately obtain the crop growth status of small and medium-sized areas. This research utilized multispectral remote sensing information collected via UAV, alongside the WOFOST (World Food Studies) model, to evaluate the growth of summer maize within the experimental site. By utilizing vegetation indices derived from the UAV remote sensing data, the LAI (Leaf Area Index) of summer maize was ascertained, which further revealed a robust correlation between LAI and NDVI (Normalized Difference Vegetation Index). The model parameters were calibrated using crop and soil data from the Irrigation Experiment Station of Northwest A&F University. Verification indices revealed good consistency with a normalized root mean square error (nRMSE) of around 25 %, indicating that the model was suitable for simulating summer maize growth in this region. The ensemble Kalman filter algorithm was utilized to assimilate the remote sensing observation data with the WOFOST model, and the results showed that assimilation improved the accuracy of the yield simulation for each treatment. The optimal assimilation frequency should be more than three times, and assimilation during the flowering and grain-filling stages of summer maize could obtain better yield simulation results. By assimilating remote sensing observation data with 33 years of historical meteorological data from the Yangling Meteorological Station using the ensemble Kalman filter algorithm, this study predicted the yield and obtained a relative error of less than 5 %. The predicted yield value obtained on September 6 was more accurate than the predicted yield obtained at other times.

Study on the impact of low‐temperature stress on winter wheat based on multi‐model coupling

AbstractCrop growth models, such as the WOrld FOod STudies (WOFOST) model, mimic the mechanistic processes involved in crop development, growth, and yield production. The accuracy of simulation is decreased in unfavorable low‐temperature settings because these models do not accurately represent crop response processes in low‐temperature stress. Enhancing the WOFOST crop growth model's accuracy in simulating crops' responses to cold temperatures is the aim of this work. Given its vulnerability to low temperatures, the inquiry uses winter wheat in Henan Province as a focal point. It integrates the WHEATGROW wheat phenology model with the Frost model of Lethal Temperature 50 (FROSTOL) inside the framework of the crop growth model. This link aims to improve simulation accuracy and supplement the model's mechanisms, particularly when it comes to the impact of low temperatures on crop development. The study uses Long Short‐Term Memory networks to build a yield model that integrates remote sensing data with information from simulated crop models. Under low temperatures, the leaf area index, total above ground biomass, and total weight of storage organs of the model WWF—which combines FROSTOL and WHEATGROW with WOFOST—show a considerable decline. It was discovered that there is a greater improvement in simulation accuracy of the linked model WWF relative to the WOFOST model in frost years than in normal years, based on a comparison analysis between typical frost years and normal years. To be more precise, the improvement is 8.03% in frost years and 1.98% in regular years. When all is said and done, the coupled model advances our knowledge of how winter wheat is impacted by low temperatures.

Effects of warming and drought on growth and development of soybean in Hailun region.

As a result of global warming, drought, flooding, change in the rainfall pattern, etc. occur frequently. All these natural disasters could cause serious damage to the food security. Soybean is one of the most important oil crops in China. In recent years, the changing climate has brought many uncertain risks to the growth and production of soybean. In this study, based on the local meteorological, soil, and soybean growth-related experimental data, the effects of high temperature and drought stress on soybean were tested. The test parameters were leaf area index (LAI) and dry matter weight, while the analytical tool used was World Food Studies Model crop model. The research was carried out in Hailun City, Heilongjiang Province, China. The results showed that warming stress shortened the growth period of soybean and reduced the LAI and dry matter accumulation. On the other hand, drought stress also showed a significant impact on the growth period as well as reduced LAI and dry matter accumulation. Comparing the whole growth as well as the flowering-stage to seed-filling-stage treatments of soybean, the results were found very similar. It indicated that the soybean growth from flowering to seed-filling stage was strongly affected by the external environmental factors. The high temperature and drought disasters in the fruiting stages would have a greater impact on the growth and production of soybean crop.

Simulating the impact of subsurface pipe drainage systems on crop water productivity at a regional scale in the upper Yellow River Basin

AbstractSubsurface pipe drainage (SPD) could provide a suitable environment for crop production in arid and semi‐arid agricultural areas; however, less attention has been given to the design layout of SPD on the regional scale in Hetao Irrigation District (HID). In this study, the appropriate layout of the final subsurface pipe was determined using the distributed SWAP‐WOFOST (Soil–Water–Atmosphere–Plant and WOrld FOod STudies) model in HID. Subsequently, the impact of the proper SPD on crop yield and water productivity (WP) were evaluated. Finally, the crop zoning was adjusted to further enhance crop WP, and the corresponding newly added cultivated land area was preliminarily estimated based on the suggested SPD. The results indicated that after zoning in the suggested SPD, the annual average yields of spring wheat, spring corn and sunflower increased by 19%, 7% and 11%, respectively, and WP improved by 13%, 5% and 10%, respectively, compared to the traditional layout. The newly added cultivated land area was estimated to be approximately 3489 ha through the replacement of traditional end ditches with subsurface pipes in HID. Therefore, the suggested subsurface pipe designs and cropping pattern adjustment improved crop growth and led to an increase in the cultivated land area in HID.

A method for estimating yield of maize inbred lines by assimilating WOFOST model with Sentinel-2 satellite data.

Maize is the most widely planted food crop in China, and maize inbred lines, as the basis of maize genetic breeding and seed breeding, have a significant impact on China's seed security and food safety. Satellite remote sensing technology has been widely used for growth monitoring and yield estimation of various crops, but it is still doubtful whether the existing remote sensing monitoring means can distinguish the growth difference between maize inbred lines and hybrids and accurately estimate the yield of maize inbred lines. This paper explores a method for estimating the yield of maize inbred lines based on the assimilation of crop models and remote sensing data, initially solves the problem. At first, this paper analyzed the WOFOST(World Food Studies)model parameter sensitivity and used the MCMC(Markov Chain Monte Carlo) method to calibrate the sensitive parameters to obtain the parameter set of maize inbred lines differing from common hybrid maize; then the vegetation indices were selected to establish an empirical model with the measured LAI(Leaf Area Index) at three key development stages to obtain the remotely sensed estimated LAI; finally, the yield of maize inbred lines in the study area was estimated and mapped pixel by pixel using the EnKF(Ensemble Kalman Filter) data assimilation algorithm. Also, this paper compares a method of assimilation by setting a single parameter. Instead of the WOFOST parameter optimization process, a parameter representing the growth weakness of the inbred lines was set in WOFOST to distinguish the inbred lines from the hybrids. The results showed that the yield estimated by the two methods compared with the field measured yield data had R2: 0.56 and 0.18, and RMSE: 684.90 Kg/Ha and 949.95 Kg/Ha, respectively, which proved that the crop growth model of maize inbred lines established in this study combined with the data assimilation method could initially achieve the growth monitoring and yield estimation of maize inbred lines.

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.

Crop production on the Chinese Loess Plateau under 1.5 and 2.0 °C global warming scenarios

Global warming is a crucial factor affecting crop production in ecologically vulnerable areas. Warming-induced changes in the yields of different crops could pose significant challenges to food security and sustainability assessment. In this study, the World Food Studies model and a remote sensing product assimilation algorithm were used to develop a spatially explicit crop assimilation model applicable to the Loess Plateau of China. The model was used to simulate potential changes in actual yields and yield gaps for winter wheat and maize under three typical climate scenarios (Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, and RCP 8.5) from 2016 to 2060. Average yields increased in both winter wheat (2.38 %–4.96 %) and maize (5.41 %–6.85 %), with maize (RCP 4.5 > RCP 8.5 > RCP 2.6) more adapted to climate warming than winter wheat (RCP 2.6 > RCP 8.5 > RCP 4.5) in terms of yield increase rate. The yield increase and yield gap for winter wheat decreased most significantly in RCP2.6 (−2.28 %). Maize yield did not exceed 80 % of the potential yield in any scenario. The average phenological periods for winter wheat and maize are predicted be 2–4 and 9–16 days earlier, respectively. Crop yields were negatively correlated with radiation and yield gaps were positively correlated with precipitation. Future climate change will likely cause dramatic interannual crop yield fluctuations. Winter wheat is predicted to experience yield stagnation after 2050, whereas maize production potential will increase briefly before experiencing a long-term decline in growth. The results of this multi-scenario simulation assessment of crop production provide scientific support for implementing climate-adapted crop management strategies and integrated dry-crop-irrigated agriculture to meet food security objectives in this ecologically fragile area. We recommend integrated management measures to ensure regional food security through crop variety improvement, irrigation regulation, and planting structure optimization.

Long time-series variation of crop yield under drought stress and drought vulnerability curves in Songnen Plain, Northeast China

It is of practical significance to reveal the long-term characteristics of drought and its impacts on crop yield in Songnen Plain, an important commodity grain base in Northeast China. This study firstly explored the long time-series variation of Modified Soil Water Deficit Index (MSWDI) and crop yield at multiple sites in Songnen Plain during 1960–2014 based on numerical simulation with the World Food Studies (WOFOST) model. This study then revealed the mutation of the drought and crop yield with three mutation point test methods, and thereafter explored their relationship between the drought and crop yield with the cross wavelet transform (XWT) and drought vulnerability curve. Results suggested the precipitation was at the lowest level in the 1970 s, which significantly increased in the 1980 s and thereafter exhibited a slow declining trend. Mutation points of drought conditions occurred at multiple stations in 1983, and the crop yields also showed obvious mutation in 1983 or 1984 at two stations. Besides, the drought and crop yield had significant 2–4 year resonance cycles during 1978–1980 at four stations. In particular, periods with high correlation between these two variables were generally years prone to abrupt changes. In addition, determination coefficients of the drought vulnerability curves based on the linear function, cubic curve function and hyperbolic tangent function reached 0.55, 0.65, and 0.64, respectively, suggesting the cubic curve function performed best. The drought vulnerability curve based on the cubic curve function suggested the crop yield loss showed a firstly decreasing and then increasing trend, which may be related to the compensation mechanism of crops. Overall, combination of the mechanistic drought index and crop models provides an effective way to reveal impacts of drought stress on crop yield and offer support information for coping with drought.

Modelling growth of chili pepper (Capsicum annuum L.) with the WOFOST model

CONTEXTChili pepper (Capsicum annuum L.) is one of the most economically and agriculturally important, and relatively nutrient-dense, vegetables that has, to date, received little attention in model studies relevant to dry matter production and nutrient-uptake predictions. There is an urgent need for models to analyse the potential impacts of climate change, as well as responsive adaptation options, while simultaneously optimising productivity against fertilizer use to reduce nutrient pollution. OBJECTIVEWe adapted the WOrld FOod STudies (WOFOST) crop growth simulation model for chili pepper (WOFOST-Chili) to quantify dry matter production as a function of fertilizer management, climate, and soil conditions. METHODSWe used 2021 field trial data under optimal growing conditions in southwestern China to parameterise and calibrate WOFOST-Chili. The model was tested under no-fertilizer conditions and further validated with data from 2019 and 2020. In addition, a sensitivity analysis over the three consecutive years was performed. RESULTS AND CONCLUSIONSOverall, the developed WOFOST-Chili model shows good simulations of chili growth dynamics in response to nitrogen (N) fertilization, both on biomass assimilation (rRMSE = 0.07 for total aboveground production; rRMSE = 0.06 for fruit dry weight) and nutrient uptake (rRMSE = 0.46 for leaf N amount; rRMSE = 0.29 for fruit N amount). Additionally, model robustness is increased by the sensitivity analysis of crop initialisation (e.g., biomass and leaf area index at transplanting) and climate-dependent parameters (e.g., temperature sums determining development rate and light use efficiency determining productivity), with the resulting wider simulation range covering more observations. This good performance is only limited by a weaker leaf area index (LAI) simulation (rRMSE = 0.76), which is partially attributed measurement limitations (e.g., equipment, weather conditions and labour/time constraints). Model validation confirms good performance under potential conditions, which is slightly reduced under nutrient-limited conditions. SIGNIFICANCEThese findings improve our understanding of yield-nutrient interactions of chili pepper. They provide insight on expanding the application of crop models originally designed for cereals to non-Gramineae vegetables, while calling for future improvement of model accuracy under different fertilizer application strategies.

Evaluation of downscaling seasonal climate forecasts for crop yield forecasting in Zimbabwe

Meteorology and weather forecasting are crucial for water-limited agriculture. We evaluate the added value of downscaling seven-months global deterministic seasonal forecasts from the Climate Forecast System version 2 (CFSv2) using the Weather, Research and Forecasting (WRF) model over Zimbabwe for ten growing seasons (2011–2021). Downscaling reduces the area of significant differences between forecasted and observed total seasonal rainfall. Downscaling also improves the score for droughts as measured through the standardized precipitation index and 3-class method. Yield forecasts by the WOrld FOod STudies (WOFOST) model reveal that downscaling improves the estimated growing season evolution and maize yield in all studied regions across the country. For the main maize production region Karoi, the bias, root mean square error, mean absolute error and mean absolute percentage error reduce by 33% (0.2 ton/ha), 27% (0.4 ton/ha), 31% (0.4 ton/ha) and 27% (8.3 %) respectively by downscaling. Hence we illustrate that downscaling the deterministic seasonal forecasts may assist in food security in a crucial area in southern Africa.

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.

An Improved Approach of Winter Wheat Yield Estimation by Jointly Assimilating Remotely Sensed Leaf Area Index and Soil Moisture into the WOFOST Model

The crop model data assimilation approach has been acknowledged as an effective tool for monitoring crop growth and estimating yield. However, the choice of assimilated variables and the mismatch in scale between remotely sensed observations and crop model-simulated state variables have various effects on the performance of yield estimation. This study aims to examine the accuracy of crop yield estimation through the joint assimilation of leaf area index (LAI) and soil moisture (SM) and to examine the scale effect between remotely sensed data and crop model simulations. To address these issues, we proposed an improved crop data-model assimilation (CDMA) framework, which integrates LAI and SM, as retrieved from remotely sensed data, into the World Food Studies (WOFOST) model using the ensemble Kalman filter (EnKF) approach for winter wheat yield estimation. The results showed that the yield estimation at a 10 m grid size outperformed that at a 500 m grid size, using the same assimilation strategy. Additionally, the winter wheat yield estimation accuracy was higher when using the bivariate data assimilation method (R2 = 0.46, RMSE = 756 kg/ha) compared to the univariate method. In conclusion, our study highlights the advantages of joint assimilating LAI and SM for crop yield estimation and emphasizes the importance of finer spatial resolution in remotely sensed observations for crop yield estimation using the CDMA framework. The proposed approach would help to develop a high-accuracy crop yield monitoring system using optical and SAR retrieved parameters.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.