Simulate Crop Yield Research Articles

Simulating water-limited potato yields across the Netherlands with (SWAP-)WOFOST: Experimentation, model improvement and evaluation

Water availability explains a large part of the spatial and temporal yield variability of ware potato in the Netherlands. Climate change is projected to lead to greater variability in water availability. Therefore, an accurate simulation of crop yield as a function of water availability is of high importance. Many crop models can simulate water-limited potato yields, but as detailed experimental data on drought and oxygen stress are scarce, models are often not well calibrated and evaluated. We set up a large experiment and an on-farm observational trial to calibrate and evaluate the simulation of water-limited yield of different potato cultivars across the Netherlands. In addition, we investigated the required model complexity to accurately simulate water-limited potato yields. The crop growth model WOFOST was used for simulations either as a single model or coupled with SWAP. Yields were simulated applying either only drought stress or by including both drought and oxygen stress. We employed two methods for simulating oxygen stress in SWAP-WOFOST. The calibration improved accuracy of simulated water-limited yields as demonstrated in our model evaluation. Both on sandy and clayey soils, water-limited yields were best simulated using SWAP-WOFOST while simulating oxygen stress using a process-based method in combination with a rooting density that decreases linearly with rooting depth. On sandy soils, the bottom boundary condition of free drainage was applicable. On clayey soils measured groundwater levels (or soil water pressure heads when measurements are not available) should be used as bottom boundary condition. On sandy soils both WOFOST and SWAP-WOFOST may be used, as results were similar. On clayey soils, WOFOST is less suitable, as it cannot simulate capillary rise. SWAP-WOFOST however has many options and parameters, which can result in large differences in results. Hence, a careful model set up and evaluation is required for each application.

Land Surface Modeling as a Tool to Explore Sustainable Irrigation Practices in Mediterranean Fruit Orchards

AbstractIrrigation strongly influences land‐atmosphere processes from regional to global scale. Therefore, an accurate representation of irrigation is crucial to understand these interactions and address water resources issues. While irrigation schemes are increasingly integrated into land surface models, their evaluation and further development remains challenging due to data limitations. This study assessed the representation of field‐scale irrigation using the Community Land Model version 5 (CLM5) through comparison of observed and simulated soil moisture, transpiration and crop yield. Irrigation was simulated by (a) adjusting the current irrigation routine and (b) by implementing a novel irrigation data stream that allows to directly use observed irrigation amounts and schedules. In a following step, the effect of different irrigation scenarios at the regional scale was simulated by using this novel data stream. At the plot scale, the novel irrigation data stream performed better in representing observed SM dynamics compared to the current irrigation routine. Nonetheless, simplifications in crop and irrigation representation and uncertainty in the relation between water stress and yield currently limit the ability of CLM5 for field‐scale irrigation scheduling. Still, the simulations revealed valuable insights into model performance that can inform and improve the modeling beyond the field scale. At regional scale, the simulations identified irrigation priorities and potential water savings. Furthermore, application of LSMs such as CLM5 can help to study the effects of irrigation beyond water availability, for example, on energy fluxes and climate, thus providing a powerful tool to assess the broader implications of irrigation at larger scale.

Evaluation of a combined drought indicator against crop yield estimations and simulations over the Argentine Humid Pampas

Evaluation of a combined drought indicator against crop yield estimations and simulations over the Argentine Humid Pampas

A Review of the Research Status and Prospects of Regional Crop Yield Simulations

A Review of the Research Status and Prospects of Regional Crop Yield Simulations

Climatic Suitability for Cocoa Production in Nigeria

Cocoa is one of the major cash crops that contribute more to the gross domestic product (GDP) of Nigeria and West Africa as a whole. Its contribution to the economic development of Africa and the world in general cannot be over emphasized. However, it has been discovered that cocoa is susceptible to changes in climatic parameters which has made it difficult to be grown in every part of the country, hence, the need to underscore the areas that are climatically suitable for the production of cocoa in Nigeria. This study was carried out to determine the climatic suitability pattern for the production of cocoa in Nigeria. A series of multi-year climate and crop yield simulations were performed for the present day (1976-2005), Mid-term (2021-2050) and long-term (2071-2100) under RCP 4.5 emission scenario using Fuzzy Logic Method (FLM) in ArcGIS (10.4.1). The results showed that some regions that are climatically suitable for cocoa production in the present day have not been explored. The regions with moderately suitability was projected to decrease the year 2050 while regions with marginally suitable, will increase by the year 2050. The remaining land that are not suitable will also decrease by the year 2050. The very suitable region was projected to increase by the year 2050 and by 2100. Also, a land area suitable and moderately suitable for cocoa production was projected to increase in land mass by the year 2050 and 2100. However, the region with marginally suitability will decrease by 2050 and year 2100. Meanwhile, the area that is not suitable by 2050 will increase by the year 2100. Which means there is a decrease in climatic suitability of cocoa by 2100 as projected.

Both yields of maize and soybean and soil carbon sequestration in typical Mollisols cropland decrease under future climate change: SPACSYS simulation

Although Mollisols are renowned for their fertility and high-productivity, high carbon (C) losses pose a substantial challenge to the sustainable provision of ecosystem services, including food security and climate regulation. Protecting these soils with a specific focus on revitalizing their C sequestration potential emerges as a crucial measure to address various threats associated with climate change. In this study, we employed a modeling approach to assess the impact of different fertilization strategies on crop yield, soil organic carbon (SOC) stock, and C sequestration efficiency (CSE) under various climate change scenarios (baseline, RCP 2.6, RCP 4.5, and RCP 8.5). The process-based SPACSYS model was calibrated and validated using data from two representative Mollisol long-term experiments in Northeast China, including three crops (wheat, maize and soyabean) and four fertilizations (no-fertilizer (CK), mineral nitrogen, phosphorus and potassium (NPK), manure only (M), and chemical fertilizers plus M (NPKM or NM)). SPACSYS effectively simulated crop yields and the dynamics of SOC stock. According to SPACSYS projections, climate change, especially the increased temperature, is anticipated to reduce maize yield by an average of 14.5% in Harbin and 13.3% in Gongzhuling, and soybean yield by an average of 10.6%, across all the treatments and climatic scenarios. Conversely, a slight but not statistically significant average yield increase of 2.5% was predicted for spring wheat. SOC stock showed a decrease of 8.2% for Harbin and 7.6% for Gonghzuling by 2,100 under the RCP scenarios. Future climates also led to a reduction in CSE by an average of 6.0% in Harbin (except NPK) and 13.4% in Gongzhuling. In addition, the higher average crop yields, annual SOC stocks, and annual CSE (10.15–15.16%) were found when manure amendments were performed under all climate scenarios compared with the chemical fertilization. Soil CSE displayed an exponential decrease with the C accumulated input, asymptotically approaching a constant. Importantly, the CSE asymptote associated with manure application was higher than that of other treatments. Our findings emphasize the consequences of climate change on crop yields, SOC stock, and CSE in the Mollisol regions, identifying manure application as a targeted fertilizer practice for effective climate change mitigation.

Plastic film mulching increases crop yields and reduces global warming potential under future climate change

Identifying suitable cropping systems and planting areas with high yields and low greenhouse gas (GHG) emissions to adapt to climate change is essential for sustainable agricultural development. The DeNitrification-DeComposition (DNDC) model and the Coupled Model Inter-comparison Project Phase 6 (CMIP6) dataset were used to evaluate the impact of climate scenarios on the crop yields and global warming potential (GWP) of maize, winter wheat, and potato under plastic film mulching (PM) and without mulching (CK) systems on the Loess Plateau for 2020–2100. The result showed that the DNDC model could simulate crop yields and carbon dioxide, nitrous oxide, and methane emissions (adjusted R2 > 0.55, normalized root mean square error (nRMSE) < 0.32). Under multiple emission scenarios, the yields of maize and winter wheat increased linearly with time (p < 0.05), the GWP decreased linearly with time (p < 0.05), and the coefficients of variation of both were lower for the PM than CK, indicating that the impact of future climate change on PM system was small and can achieve synergy between food security and carbon reduction. The results of regional divergence characteristics suggested that areas with precipitation below 400 mm were suitable for maize cultivation, especially the PM system, while areas with precipitation above 400 mm were suitable for winter wheat cultivation – both of which could achieve the goals of high yield and low GHG emissions. This study presents an overview of spatiotemporal changes in production and ecological effects, providing a basis for developing agricultural management practices and cultivation areas to address climate change, and could promote the development of dryland agroecosystems in China in a green and sustainable direction.

Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE &lt; 0.5 t/ha, RMSE &lt; 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

A framework for modeling an agronomic system's vulnerability to climate change with reflections from the Caspian coastal agro-ecological zone of Iran.

Assessing the vulnerability of different sectors to climate change has great importance in determining the appropriate adaptation measures to deal with climate change impacts on a river basin scale. In this research, using a framework for modeling the agronomic system vulnerability to climate change, vulnerability assessment under different scenarios was conducted for the Gorganrud River Basin located in the agro-ecological zone of the Caspian coastal plain of Iran. Considering exposure, susceptibility, and lack of resilience components, 12 indicators were chosen and quantified for both agronomic-environmental and socio-economic aspects. The SSM-iCrop2 model was used to simulate crop yield under current and climate change scenarios across the basin. The analysis indicates that in the current condition, the vulnerability level is different across the watersheds of the Gorganrud River Basin. By applying the climate change scenarios, agronomic system vulnerability would increase in the basin to some extent, particularly in Madarsu and Tilabad watersheds attributed with high vulnerability (0.63 and 0.61, respectively). This justifies the need to implement adaptation plans for encountering water shortage in the future. The analysis also suggests that the vulnerability of the agronomic system for adaptation scenarios characterized by less water consumption under climate change conditions is going to be slightly different from the vulnerability under the climate change scenarios. Due to an increase in agronomic system vulnerability under climate change scenarios, coupled with the fact that most watersheds (except Chehelchai, Nardin, and Narmab) are moderately vulnerable even under current conditions, policymakers and planners should promote crop and livelihood diversification programs aiming to prevent an increase in agronomic vulnerability.

Analysis of Crop Irrigation Water Requirements and Water Scarcity Footprint in the Beijing–Tianjin–Hebei Region Based on the GeoSim–AquaCrop Model

To reduce crop-related water consumption and enhance agricultural water resource efficiency in the Beijing–Tianjin–Hebei region, this study employed the AquaCrop model to simulate crop yield and irrigation water requirements and calculated the water scarcity footprint (WSF). The results were as follows: (1) The AquaCrop model exhibited strong applicability, with R2, RMSE (Root Mean Square Error), EF (Nash–Sutcliffe model efficiency coefficient) and d values of 0.9611, 6.6%, 0.91, and 0.98 (winter wheat), and 0.9571, 5.5%, 0.95, and 0.99 (summer maize) for canopy cover simulation. Similarly, aboveground biomass simulation yielded values of 0.9661, 0.8 t/ha, 0.93, and 0.98 (winter wheat), and 0.9087, 1.3 t/ha, 0.90, and 0.98 (summer maize). Winter wheat soil moisture content simulation showed an R2 of 0.9706, RMSE of 3.7 mm, EF of 0.93, and d of 0.98. (2) The AquaCrop model simulated the winter wheat and summer maize yields and irrigation water requirements for the years 2009, 2014, and 2019, validating the scalability and spatial visualization capabilities of GeoSim in extending AquaCrop simulations. (3) Integrating the water footprint and the water resources system, this study assessed the WSFs of winter wheat and summer maize. From 2009 to 2019, winter wheat production in the region increased by 25.08%, and summer maize production increased by 37.39%. The WSF of winter wheat decreased, whereas the WSF of summer maize increased. It is recommended to reduce crop cultivation areas in regions such as Daming County, Ningjin County, and Dingzhou City while further improving irrigation water efficiency, which would facilitate the sustainable utilization of water resources in the area.

Assessing the Impact of N Fertilizer on Soybean Yield and Quality Using a Modeling Approach

HighlightsSimulating the effect of high N fertilizer rates with the CSM-CROPGRO-Soybean model for Brazil.Simulated and observed plant tissue N accumulation increased with an increase in N fertilizer rates.N use efficiency decreased with N fertilization on soybeans under long-term scenarios.Both model simulations and observations indicate only a small response in crop yield to high N fertilization.Abstract. Soybean [Glycine max (L.)] is one of the main sources of food protein in the world. To achieve a high yield and grain protein concentration, soybean requires a large amount of nitrogen (N). Many studies have evaluated N fertilization in soybean, with the hypothesis that symbiotic N fixation may not be sufficient to supply N for high-yielding soybean. This study aimed to evaluate the performance of the CSM-CROPGRO-Soybean model with field data to determine the impact of N fertilizer on soybean and explore long-term scenarios. The experiments consisted of a combination of available water management and N fertilizer levels over eight crop seasons. After calibration and evaluation, the CSM-CROPGRO-Soybean model was used to explore the soil and plant N balance. We also applied the model to explore different N fertilization rates in long-term scenarios. The results showed that the model was able to simulate soybean growth with a very good agreement for all sites (D-statistic ranging from 0.63 to 0.99). The observed and simulated crop yield gains from N fertilization were small and not proportional to the amount of N fertilizer applied. The model underestimated the effect of N fertilizer on grain protein concentration increment, but there was a consistent trend towards an increase in protein concentration observed in measured data. Simulations of soil inorganic N balance suggested very high losses of N by volatilization for urea-N fertilization treatments. The long-term scenarios showed that using N rates above 600 kg ha-1 is potentially inappropriate, and a higher crop N use efficiency was obtained with no N fertilizer. Keywords: Ammonia volatilization, Crop yield, Crop N use efficiency, CROPGRO-Soybean model, Grain protein concentration, Nitrogen balance.

Rape Yield Estimation Considering Non-Foliar Green Organs Based on the General Crop Growth Model

To address the underestimation of rape yield by traditional gramineous crop yield simulation methods based on crop models, this study used the WOFOST crop model to estimate rape yield in the main producing areas of southern Hunan based on 2 years of field-measured data, with consideration given to the photosynthesis of siliques, which are non-foliar green organs. First, the total photosynthetic area index (TPAI), which considers the photosynthesis of siliques, was proposed as a substitute for the leaf area index (LAI) as the calibration variable in the model. Two parameter calibration methods were subsequently proposed, both of which consider photosynthesis by siliques: the TPAI-SPA method, which is based on the TPAI coupled with a specific pod area, and the TPAI-Curve method, which is based on the TPAI and curve fitting. Finally, the 2 proposed parameter calibration methods were validated via 2 years of observed rape data. The results indicate that compared with traditional LAI-based crop model calibration methods, the TPAI-SPA and TPAI-Curve methods can improve the accuracy of rape yield estimation. The estimation accuracy ( R 2 ) for the total weight of storage organs (TWSO) and above-ground biomass (TAGP) increased by 9.68% and 49.86%, respectively, for the TPAI-SPA method and by 14.04% and 42.94%, respectively, for the TPAI-Curve method. Thus, the 2 calibration methods proposed in this study are of important practical importance for improving the accuracy of rape yield simulations. This study provides a novel technical approach for utilizing crop growth models in the yield estimation of oilseed crops.

The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations

Abstract. Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2–temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change.

Aiming at low nitrogen leaching diets based on nitrogen fertilizer regulatory policy: A regional bio-economic assessment of the Zayandeh-Rud river basin-Iran

Negative environmental impacts of nitrogen (N) intensive diets have triggered global debates on sustainable nitrogen management. Solutions such as dietary transitions, cropland reallocation and N Regulatory Policy (NRP) have been proposed to mitigate the adverse environmental impacts of N use in food production. However, there is still insufficient understanding of how NRPs could be designed to minimize negative environmental impact across diverse agro-ecological zones without sacrificing human dietary requirements. To increase this understanding, we evaluated the consequences of three NRP scenarios (low, moderate, and high N fertilizer rates) on the amount of livestock and non-livestock diet components as well as the associated N leaching and farmers' Gross Margin (GM) by optimizing the allocation of cropland between food and feed crops. We developed a bio-economic Interval Fuzzy Multi-Objective Programming (bio-economic IFMOP) model for the Zayandeh-Rud river basin, Iran, and a procedure that accounts for annual average availability of calories per capita, calorie sources from livestock and non-livestock components of three dietary preferences, and inequality in calorie distribution. The interaction among soil, climate and weather variability and NRPs across nine sub-regions of the case study region was handled by crop yield simulation using the DSSAT software. The solution of farmers' GM, derived from the optimization problem across possibilities of water fluctuations, was assessed to determine the uncertainty in GM. We also introduced an N leaching per Block of Distributed Calories (BDC) criterion based on solutions of supplied calories and associated N leaching. The upper bound of the moderate NRP scenario resulted in the smallest N leaching per BDC. This corresponded to ∼0.34, ∼0.34, ∼3.77 and 19.00 million BDC of meat, dairy, wheat and potato, respectively. Also, the upper bound of this scenario satisfied the lowest instability in farmers’ GM against water fluctuation compared with low and high NRP scenarios. The affordable volume of N leaching per BDC varied across sub-regions between [1.53,3.49], [1.52,3.33], [0.76,0.99] and [0.05,0.08] kg for meat, dairy, wheat and potato, respectively. Our results highlighted both optimistic and pessimistic prospects of producing low N leaching diets. The approach of this study could also be applied to other regions and countries.

Energy crop yield simulation and prediction system based on machinelearning algorithm

Energy crop yield simulation and prediction system based on machinelearning algorithm

Assessing the impact of meteorological and agricultural drought on maize yields to optimize irrigation in Heilongjiang Province, China

Agriculture is an essential support for social and economic development, most affected by weather and climate. Drought in agriculture significantly threatens food security and social stability. Understanding drought variability in the cultivated land can lay the foundation for crop management. Considering the effects of drought on the actual crop growth process and precise irrigation under limited water resources is a great challenge. In this study, standardized precipitation evapotranspiration index (SPEI) and soil water content were used to study the spatiotemporal characteristics of drought during maize growth in Heilongjiang Province. The distributed AquaCrop model and grey theory were used to study the relationship between irrigation, drought and maize yield at the grid scale, and the current irrigation effect was evaluated by simulating the comparison of crop yield under non-irrigation and irrigated conditions, so as to provide a basis for optimizing the irrigation system in the future. In addition, Future drought trends were also examined using the Mann-Kendall statistical test, Sen's slope, and Hurst index. The results are as follows: (1) Based on remote sensing inversion and distributed AquaCrop model, the relationship between drought and crop yield can be studied at grid scale, which can refine the impact of drought on crop yield in different cells. (2) Irrigation can increase crop yields by at least 20–40% in about 60% of areas in the Heilongjiang Province. (3) In response to the drought characteristics of alternating wet and dry variations or consistent wet and dry variations in different months, inter-regional water transfer can be used to cope with or ensure increased irrigation in drought-prone months (May and June) to achieve water conservation and efficiency. Evaluating the current irrigation effect by simulating crop yield under non-irrigated conditions and crop yield under irrigated conditions can provide a better theoretical basis for optimizing irrigation systems, in order to improve regional crop yields and reduce drought risks.

Cross-Validation Strategy Impacts the Performance and Interpretation of Machine Learning Models

Abstract Machine learning algorithms are able to capture complex, nonlinear, interacting relationships and are increasingly used to predict agricultural yield variability at regional and national scales. Using explainable artificial intelligence (XAI) methods applied to such algorithms may enable better scientific understanding of drivers of yield variability. However, XAI methods may provide misleading results when applied to spatiotemporal correlated datasets. In this study, machine learning models are trained to predict simulated crop yield from climate indices, and the impact of cross-validation strategy on the interpretation and performance of the resulting models is assessed. Using data from a process-based crop model allows us to then comment on the plausibility of the “explanations” provided by XAI methods. Our results show that the choice of evaluation strategy has an impact on (i) interpretations of the model and (ii) model skill on held-out years and regions, after the evaluation strategy is used for hyperparameter tuning and feature selection. We find that use of a cross-validation strategy based on clustering in feature space achieves the most plausible interpretations as well as the best model performance on held-out years and regions. Our results provide the first steps toward identifying domain-specific “best practices” for the use of XAI tools on spatiotemporal agricultural or climatic data. Significance Statement “Explainable” or “interpretable” machine learning (XAI) methods have been increasingly used in scientific research to study complex relationships between climatic and biogeoscientific variables (such as crop yield). However, these methods can return contradictory, implausible, or ambiguous results. In this study, we train machine learning models to predict maize yield anomalies and vary the model evaluation method used. We find that the evaluation (cross validation) method used has an effect on model interpretation results and on the skill of resulting models in held-out years and regions. These results have implications for the methodological design of studies that aim to use XAI tools to identify drivers of, for example, crop yield variability.

Identification of a spatial distribution threshold for the development of a solar radiation model using deep neural networks

We propose an approach to develop a solar radiation model with spatial portability based on deep neural networks (DNNs). Weather station networks in South Korea between 33.5–37.9° N latitude were used to collect data for development and internal testing of the DNNs, respectively. Multiple sets of weather station data were selected for cross-validation of the DNNs by standard distance deviation (SDD) among training sites. The DNNs tended to have greater spatial portability when a threshold of spatial dispersion among training sites, e.g. 190 km of SDD, was met. The final formulation of the deep solar radiation (DSR) model was obtained from training sites associated with the threshold of SDD. The DSR model had RMSE values <4 MJ m−2 d−1 at external test sites in Japan that were within ±6° of the latitude boundary of the training sites. The relative difference between the outputs of crop yield simulations using observed versus estimated solar radiation inputs from the DSR model was about 4% at the test sites within the given boundary. These results indicate that the identification of the spatial dispersion threshold among training sites would aid the development of DNN models with reasonable spatial portability for estimation of solar radiation.

Sensitivity analysis of land and water productivities predicted with an empirical and a process-based root water uptake function

Rootzone hydraulic conditions govern root water uptake and transpiration under drought stress. Process-based approaches to predict the soil water status are advocated for an improved simulation of soil hydrology and crop yield. We investigated the sensitivity to system parameters in root water uptake simulation using a process-based function (MFlux) and an empirical function (Feddes) embedded in the SWAP hydrological model, applied to irrigated pasture scenarios in New Zealand. Data from two locations and three soils were used to simulate 42 growing seasons. The sensitivity analysis of both Feddes and MFlux parameters was performed for a rainfed and two irrigated scenarios, one triggering irrigation based on relative evapotranspiration (I–ETr), the other based on common practice using total available water (I–TAW). Results confirm that some parameters of the MFlux function are more sensitive than those from the Feddes function and both functions support the I–ETr criterion to optimize the water use in grazed pastures in New Zealand.

Forecasting spring maize yield using vegetation indices and crop phenology metrics from UAV observations

AbstractEarly and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient‐boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R2 and lowest RMSE of 0.894, 1.238 × 103 kg ha−1, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R2, RMSE, and MAE of 0.892, 1.189 × 103 kg ha−1, and 9.150 × 102 kg ha−1, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale.