Effects of automatic multi-objective optimization of crop models on corn yield reproducibility in the U.S.A.

The purpose of this study was to relate the temporal and spatial variability of corn ( Zea mays L.) grain yield on a Typic Fragiochrept soil on a hillslope to soil properties and topographic features. Corn grain yields were sampled from a field that measured 280 by 150 m using a grid and five transects. One‐hundred forty yield measurements were taken on the grid (1983–1985) and 190 measurements on the transects (1984 and 1985) from plots 5.3 m long and two corn rows wide. Measurements of soil surface elevation, soil organic matter (OM), P, and K contents were also taken at the grid plot locations. These data were analyzed using the methods of spectral analysis. Yield spatial and temporal variability was strongly related to surface undulations and the value of surface curvature was found to be a useful parameter to quantify variations in topography. The intra‐annual differences in weather had the largest effect on grain yield at locations where the magnitude of curvature was large. Where the magnitude of curvature was small, the correlations of yield for the wet (1984) and dry (1985) years were highly significant. Yields correlated with soil P, K, and OM only in the dry year, 1985. Yields in the relatively dry years (1983 and 1985) correlated with depth to fragipan. Elevation data helped us interpret the spatial and temporal variability of grain yield by separation of areas with convex curvature from areas with concave curvature.

Impact of climatic variables on the spatial and temporal variability of crop yield and biomass gap in Sub-Saharan Africa- a case study in Central Ghana

Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units

The Environmental Policy Integrated Climate (EPIC) model has been used on the semiarid temperate Canadian Prairies to estimate crop yield, soil erosion loss, and water and nitrate dynamics. While its estimates of long-term average yields are accurate for most purposes, additional model development is desirable to fully reflect year-to-year variability. We tested the precision of EPIC (version 5300) in estimating mean yields and in replicating yearly yield variability as influenced by the potential evapotranspiration (PET) method, using field data from a 27-yr crop rotation experiment at Swift Current, Saskatchewan. Rotations tested ranged from continuous wheat (Triticum aestivum L.) to fallow-wheat-wheat. Mean estimated yields were compared with measured yields (MY) and detrended yields (DY). Estimated yields and MYs were further compared by regression, ratio of variances due to lack-of-fit and to experimental errors (R), and model efficiency (EF). Mean yields estimated using the Penman-Monteith and the Priestly-Taylor PET methods resulted in significant underestimations, associated with high annual PET values, and were not analysed further. The Hargreaves (H) and Baier-Robertson (BR) PET methods resulted in mean yields not different than MY or DY for most cases, especially the BR method. EPIC with the H method accounted for 18 to 66% of the variability in annual yield estimation, whereas the BR method accounted for 29 to 60%. These were slightly, but not significantly, lower than results obtained with regionally derived statistical crop models. Overall EPIC with the BR PET method provided yield estimates accurate and precise enough for long term studies. The relatively high R and low EF values obtained, though, suggest further improvements in EPIC are necessary to better replicate yearly yield variability. Analysis of yield residuals indicated that EPIC may not be simulating accurately enough the water balance and its effects throughout the off-season and in the early part of the growing season. Key words: Semiarid temperate climate, crop model, potential evapotranspiration

Spatiotemporal variability in vineyard soil water content (SWC) can affect yield and berry composition. The objectives of this study were to examine spatial variability in yield and berry composition over time and to quantify the contribution of spatial variability in SWC to these parameters. Yield, Brix, tannins, and total phenols were collected at the end of the berry ripening stage for 135 georeferenced points in 2012 and for 147 points in 2013 and 2014 in a Merlot vineyard in Gansu province, China. Volumetric SWC was measured multiple times at those points at different growth stages in each year. Spatial variation in yield showed moderate and similar spatial heterogeneity for different years, but the variation in berry composition was not consistent among years. Spatial variation in yield in 2012 and 2013 was best described by an exponential variogram model, but there was no spatial structure of yield in 2014. Low SWC at 0 to 20 cm depth corresponded to higher yield than did high SWC, and the opposite results were found for 40 to 60 cm. Spatiotemporal distribution of SWC explained more than 35% of total variation in yield and berry composition in the relatively dry year of 2012, but less than 10% in the relatively wet year of 2014. The contribution of spatiotemporal distributions of SWC to spatial variability in yield and berry composition varied according to field and weather conditions. This study provides a basis for developing site-specific management measures for better economic return.

Agricultural fields have been always considered as uniform entities and managed accordingly. However, uniform agronomic management in fields where spatial variability is present, is economically and environmentally inefficient. The study was carried out on a 18 ha field located in Melfi (PZ, Basilicata), Southern Italy during six years of alternate cultivation of wheat and forage. Spatial maps of grain yield, normalized difference vegetation index (NDVI) and topography were collected. The spatial maps were used to define spatial and temporal yield variability and to identify two stable zones within the field, “low yield stable” (LS) and “high yield stable” (HS). Short fallow rainfall was correlated with grain yield of HS. Both zones were negatively correlated to vegetative growing season (Dec-Gen-Feb cumulative rainfall). Wheat yield production in Mediterranean environment is highly affected by rainfall and amount of soil water stored into the soil before and during the growing season. The objectives of this study were to identify spatio-temporal stable areas throughout the field, understanding the influence of rainfall on wheat yield. Starting from this map, prescription maps are produced to experiment the distribution of variable-dose nitrogenated fertilizers with the Kverneland GEOSPAT Exacta TL Fertilizer spreader. This operation in the case of cereal crops, today still represents a highly impacting intervention from the economic and environmental point of view.

Maintaining agricultural productivity into the future is one of the most important goals of our society. The world's vast grain production areas in arid environments have a high sensitivity to climate change, thus demanding accurate future predictions. Management and decision making depends on an understanding of complex spatial and temporal interactions between rainfall, temperature and soils, but detailed soil information is extremely costly and generally unavailable. Crop phenology, the timing of plant growth and developmental stages, is the combined response to environmental factors such as soil, water and temperature. Crop phenology plays an important role in crop growth models and agronomic management. In Mediterranean environments like South Australia, water is the driving factor for crop growth. In these rain-fed farming systems, water availability largely depends on rainfall amount and seasonality and the capacity of the soil to retain rainfall inputs and hence make water available for crops. Plant Available Water Capacity of soil (PAWC) is an important measure of the spatial and temporal variability of crop growth and yield, but there is a paucity of detailed maps at a management relevant spatial resolution and extent. In this study, different phenological metrics derived from MODIS NDVI imagery were assessed over sites of known PAWC and cropping history. We used the metrics: • NDVI at the onset of greenness (Onset) and time of Onset (OnsetT) • NDVI at peak greenness (MaxV) and time of maximum greenness (MaxT) • The length of the growing season (WidthGS) • Rate of greenup (GURate) The green up rate of the curve (GU-rate) showed the most consistent difference between soils of different PAWC. The Rate of Green up is higher for the soil points with relatively low PAWC. Some of the variability in the difference between sites with low and high PAWC can be explained with rainfall amount and seasonality but the relationship is complex. The results indicate that the crop phenology derived from MODIS satellite imagery is consistently different and may therefore provide useful information about site conditions, which would allow improvements to the spatial detail in soil maps.

Water erosion is the major process responsible for soil loss in Central Tunisia. It represents a main risk of sedimentation which threatens the lifetime of water and soil conservation works in the region, such as hillside reservoirs. This paper focuses on the study of spatial and inter-annual variability of specific sediment yield (SSY) estimated from sediment yield (SY) of 23 small watersheds of hill reservoirs located in the Tunisian Dorsal and the Cap Bon regions. The calculated values of mean inter-annual specific sediment yield varied from 180 to 3298 t/km2/year, over a period of 12 years (from 1994 to 2006). The selected catchments were classified into three groups according to their SSY data. The SSY map, elaborated with a geographical information system (GIS), showed an important spatial variability between watersheds of the same group. Moreover, analyses of the coefficient of variation (CV) showed that 70% of the considered sites demonstrate a tendency to moderate and high inter-annual variability of SSY. In order to identify the principal factors governing variability of erosion and sedimentation, a multiple regression analysis was carried out using nine parameters characterizing morphology, topography, land cover, climate and protection works of the 23 considered sites. The generated model revealed that 85% of SSY variability is correlated to seven principal factors, which are drainage area (A), rainfall intensity recorded in 30 min (I30), percentage of water and soil conservation works, percentage of clay and carbonate in soils, vegetation land cover, overall slope index (IG) and rainfall amount (Ra). The obtained SSY results were compared to others cited in a large bibliographical synthesis of studies carried out at the planet scale. We deduced that the SSY values obtained in this study are among the relatively high series in the world. The high variability of specific erosion rates in Central Tunisia is believed to be related to the semi-arid climate, characterized by the irregularity and the severity of storms and the spatial and temporal variability of other multiple factors affecting erosion processes.

Recognizing variability in plant growth early can aid in identifying yield‐limiting factors such as soil conditions, nutrient availability, and/or environmental limitations. Active sensors have been used to gather reflectance data from crop canopies and used to calculate Normalized Difference Vegetative Index (NDVI) which has been associated with percentage ground cover, leaf area index, biomass accumulation, and nitrogen use efficiency. The objective of this study was to show that NDVI can be used to characterize spatial variability in plant growth and is correlated with grain yield. NDVI values were measured biweekly throughout the growing seasons of 2010 and 2011 in corn grown at a location with soil and topographic variability. Grain yield was measured after each growing season. Two cropping rotations were used: continuous corn, and a corn‐soybean‐small grain/soybean double crop. Differences in corn growth at different landscape positions could be identified with NDVI eight weeks following planting (P≤ 0.10). A relationship was established between NDVI and grain yield eight weeks following planting (r2> 0.80). NDVI could be used to identify the variability of grain yield in continuous corn production when measurements were taken after the accumulation of 800 to 900 growing degree days.

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy

Projecting corn and soybeans yields under climate change in a Corn Belt watershed

Wheat yield and protein content are spatially variable because of inherent spatial variability of factors affecting the yield at field scale. In Mediterranean environments, yield variability is often caused by the irregular weather pattern, particularly rainfall and by position on the landscape. The objective of this study was to determine the effects of landscape position and rainfall on spatial variability of wheat yield and protein in a rolling terrain field of Southern Italy, and to propose stable management areas through simulation modelling and georesistivity imaging in rolling landscape. The study was carried out in Southern Italy, during 2 years of wheat monoculture; extensive soil properties and in‐season plant measurements were measured. This study showed that soil water content was the main factor affecting spatial variation of yield for both years. The interactions between rainfall, topography and soil attributes increase the chances to observe yield variability among years. The principal component analysis demonstrated that for both years, soil water content explained most of the variability. The crop simulation model provided excellent results when compared with measured data with root mean square error of 0.2 t ha−1. The simulated cumulative probability function showed that the model was able to confirm the yield temporal stability of three different zones.

The cultivation of sterile giant miscanthus ( Miscanthus × giganteus , M × g ) for bioenergy and bioproducts has expanded into grain‐cropped land in the United States (US) as local markets developed for this high‐yielding perennial grass (10–30 Mg DM ha −1 ). However, the magnitude of spatial and temporal variability in yield within US Corn Belt fields, along with impacts on economic return and sustainable land management, is poorly understood. This study established a diagnostic model relating remote sensing‐derived vegetation indices to ground truth data from 105 hand‐harvested stem biomass samples, which were strategically selected to represent the full range of vegetation index observations. The high‐resolution satellite‐sensed vegetation indices captured > 90% of the yield variation measured within fields. This model was then used to predict yield variability and assess economic performance across four of the first commercial M × g fields in the Corn Belt state of Iowa, US. Significant spatial variability in biomass dry matter (DM) yields (9.3–18.1 Mg DM ha −1 ) and net profits ($83 to $1211.5 ha −1 ) was observed. All fields were profitable in all site‐years. When low profit occurred, it was explained by limited management experience of the crop in Iowa. The breakeven yield at a selling price of $130 Mg −1 varied from 9.0–12.1 Mg ha −1 at 15% moisture content (7.6–10.3 Mg DM ha −1 ). Breakeven prices ranged from $73 to $122.4 Mg −1 , matching ranges used in the Department of Energy Billion Ton Report (US Department of Energy, 2023). Notably, M × g yield and profits were commensurate with grain crops particularly with favorable precipitation. This study provides insight on the M × g management “learning curve”, performance on marginal land and in drought conditions, and demonstrates that addressing yield gaps, reducing costs, and implementing precision agriculture strategies can enhance profitability. These findings emphasize the value of remote sensing technologies in guiding sustainable and competitive commercial‐scale M × g production.

Agricultural systems exhibit a large degree of within-field yield variability. We require a better understanding of the drivers of this variability in order to optimally manage croplands. We investigated drivers of sub-field spatial variability in yield for three crops (hard red winter wheat, Triticum aestivum L. variety Langin; corn, Zea mays L.; and proso millet, Panicum milaceum L.) usings a multi-year dataset from a dryland research farm in northeastern Colorado, USA. The dataset spanned 18 2.6–4.3 ha management units, over 4 years, and included high-resolution topographic data, densely sampled soil properties, and on-site weather data. We modeled yield for each crop separately using random forest regression and evaluated model performance using spatially blocked cross-validation. The topographic position index (TPI) and increasing percent sand had a strong negative effect on yield, while the nitrogen application rate (N) and total soil carbon had strong positive effects on yield in both the wheat and millet models. Remarkably, TPI had almost as large of an effect size as N, and outperformed other more commonly used topographic predictors of yield such as the topographic wetness index (TWI), elevation, and slope. Despite the size and quality of our dataset, cross-validation results revealed that our models account for approximately one-quarter of the total yield variance, highlighting the need for continued research into drivers of spatial variability within fields.