Spatial and Temporal Variability of Corn Grain Yield on a Hillslope

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

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

Fractal analysis has been used to characterize both temporal and spatial variability in plant and soil parameters. A plant parameter of prime concern is crop yield. Consequently, the temporal variability of 10 crops commonly grown in the USA was described using fractal analysis. Average yields of nine grain crops along with fiber yield of cotton (Gossypium hirsutum L.) from 1930 to 1990 in the USA were used for semivariogram and fractal analyses. Semivariance was calculated for each crop for different year intervals (h). The slope of the regression line of log semivariance vs. log h for each crop was used to calculate fractal dimension [D = (4 — slope)/2], which is an indication of the pattern of yield variability. A small D‐value (near 1) indicates the dominance of long‐term variation, while a large D‐value (near 2) indicates dominance of short‐term variation and nondominance or lack of long‐term variation or trend. From 1930 to 1990, yield of all crops increased, ranging from about twofold in soybean [Glycine max (L.) Merr.], oat (Avena sativa L.), and barley (Hordeum vulgare L.) to about sixfold in maize (Zea mays L.). Crop improvement through plant breeding and the use of fertilizers and pesticides are presumably the main reasons for increased yield. Large yield differences were observed after 1960 for most of the crops studied, suggesting that risk resulting from year‐to‐year yield differences increased with improved yields. Fractal dimensions ranged from 1.20 to 1.47 for the crops studied, identifying long‐term trend as well as short‐term variation in yield of these crops. Rice (Oryza sativa L.) had the smallest D‐value, indicating that this crop had the least short‐term variation, while oat and soybean had the largest D, indicating greatest short‐term variation. Temporal variability in average crop yield in the USA was much smaller than typical spatial variability values reported by others for soil parameters. It appears that fractal analysis is useful in quantifying temporal variability in yield of various crops and can be applied to agronomic research to characterize temporal variations.

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.

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.

HighlightsThe greatest heterogeneity in soil water was observed at 30-60 cm depth in a corn field and 0-15 cm in a cotton field.Spatiotemporal soil water variability did not increase with increasing soil water in all soil layers in both croplands during the growing season.Water excess and water stress locations were identified in both fields. A single, temporally stable location was identified in each field, which can be used for precise uniform irrigation.Knowledge of variability and stability in soil water can be useful in determining the number and location of sensors to install in crop fields to assist irrigation scheduling decisions.Abstract. This study investigated the spatiotemporal variability and temporal stability of soil water at various depths in two croplands sown in corn and cotton during the 2018 growing season in the Tennessee Valley Region (TVR) of northern Alabama. Classical statistics and relative difference approaches were used to analyze soil water data in this study. In the corn field, the 30-60 cm depth showed the greatest variability, while the cotton field showed the greatest variability at 0-15 cm depth. A decreasing trend was noticed between mean soil water and coefficient of variation for all depths in the cotton field and at 30-60 cm depth in the corn field. However, convex upward, exponential decreasing, or no trends were found between mean soil water and standard deviation at different depths in both fields. The temporal stability analysis showed one representative sensor (S8 in corn and S1 in cotton) for the entire soil profile in both fields. Different statistical tests, i.e., Spearman’s correlation (rs), Nash-Sutcliffe efficiency (NSE), coefficient of determination (R2), etc., were used to reduce uncertainty or increase confidence in the performance of representative sensors. Among various field attributes, topography in corn and soil properties in cotton were determined as significant factors responsible for soil water variability. Crop evapotranspiration (ETc) showed significant negative weak and moderate correlations with soil water in the corn and cotton fields, respectively. However, the mean air temperature showed a significant positive correlation with soil water in the corn field and a significant negative correlation in the cotton field. Solar radiation had a significant negative correlation with soil water in the cotton field and a non-significant correlation in the corn field. Accumulated growing degree days (accGDD) showed a significant negative correlation with soil water in the corn field and a positive correlation in the cotton field. This study gives insights into soil water variability, provides useful information about temporal stability, and identifies significant factors for precision uniform irrigation scheduling. Keywords: Corn, Cotton, Croplands, Growing season, Irrigation, Soil water, Spatial and temporal variability, Temporal stability.

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.

Genetic improvement in maize (Zea mays L.) grain yield is associated with improvements in dry matter accumulation during the grain‐filling period and the ability to maintain partitioning to the grain (i.e., harvest index) when grown at higher plant population densities. Although several attributes have been identified that lead to improved dry matter accumulation during the grain‐filling period, the attributes that have enabled the maintenance of harvest index at higher plant population densities remain elusive. Using the Ontario ERA hybrids that represent five eras of genetic improvement in Ontario, we examined genetic improvement in several attributes associated with sink establishment and partitioning to the grain. We show that the number of florets on an ear initial is not influenced by plant density, nor is there any evidence of a genetic improvement in floret number. There has been genetic improvement, however, in the ability to support kernel set at a lower threshold level of dry matter accumulation and to more efficiently set kernels at lower dry matter accumulation levels, such as those experienced at higher plant densities. Genetic improvement is evident for reduced plant‐to‐plant variability for dry matter accumulation, grain yield, kernel number, and plant growth rate around silking, but most notably for grain yield. Finally, we show that genetic improvement in reduced plant‐to‐plant variability for grain yield is the result of the lower threshold dry matter levels required for seed set and the improved resource utilization, which has led to greater stability of individual plant performance in a stand.

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.

Recent precision-agriculture research has focused on use of management zones (MZ) as a method for variable application of inputs like N. The objectives of this study were to determine (i) if landscape attributes could be aggregated into MZ that characterize spatial variation in soil chemical properties and corn yields and (ii) if temporal variability affects expression of yield spatial variability. This work was conducted on an irrigated cornfield near Gibbon, NE. Five landscape attributes, including a soil brightness image (red, green, and blue bands), elevation, and apparent electrical conductivity, were acquired for the field. A georeferenced soil-sampling scheme was used to determine soil chemical properties (soil pH, electrical conductivity, P, and organic matter). Georeferenced yield monitor data were collected for five (1997–2001) seasons. The five landscape attributes were aggregated into four MZ using principal-component analysis of landscape attributes and unsupervised classification of principal-component scores. All of the soil chemical properties differed among the four MZ. While yields were observed to differ by up to 25% between the highest- and lowest-yielding MZ in three of five seasons, receiving average precipitation, less-pronounced (≤5%) differences were noted among the same MZ in the driest and wettest seasons. This illustrates the significant role temporal variability plays in altering yield spatial variability, even under irrigation. Use of MZ for variable application of inputs like N would only have been appropriate for this field in three out of the five seasons, seriously restricting the use of this approach under variable environmental conditions.

Coffee producers are ever more interested in understanding the dynamics of coffee’s spatial and temporal variability. However, it is necessary to obtain high-density yield data for decision-making. The objective of this study is to evaluate the quality of yield data obtained through a yield monitor onboard a coffee harvester, as well as to evaluate the potential of the data collected over three harvests. The yield monitor validation data showed a high correlation (above R2 0.968) when compared with the data obtained by a wagon instrumented with load cells. It was also possible to obtain yield maps for three consecutive seasons, allowing the identification of their internal variability, as well as classifying regions that show alternating yield patterns between years as the expression of the biennial yield behavior manifested inside and along the field, in addition to the spatial variability. This result indicates that, in addition to knowing the spatial yield variability, the biennial variance information must also be considered in the strategies for site-specific management. Regions that presented high yield variance should be alternated according to the productive year (high and low yield) and not only in consideration of their yield variability as on the regions with more stable yield behavior over time. The use of yield data can help the producer make more assertive decisions for crop and farm management.

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

May, W. E., Lafond, G. P., Gan, Y. T., Hucl, P., Holzapfel, C. B., Johnston, A. M. and Stevenson, C. 2012. Yield variability in Phalaris canariensis L. due to seeding date, seeding rate and nitrogen fertilizer. Can. J. Plant Sci. 92: 651–669. Concern over the year-to-year and field-to-field variability in grain yield has consistently been expressed by annual canarygrass growers in Saskatchewan. The objectives of these studies were to understand the effects of a delayed seeding date (0, 15, 30 and 45 d), seeding rate (15, 25, 35, 45, and 55 kg ha−1 of seed) and applied N fertilizer (20, 40, 60, 80, and 100 kg N ha−1) on the development and yield of annual canarygrass, to improve recommendations of best management practices in annual canarygrass and to determine the impact of these factors on yield variability in annual canarygrass. To address these objectives, three single factor field experiments were conducted, at a number of sites in Saskatchewan from 1998 to 2001. Seeding date had a large effect on grain yield. Grain yield decreased as seeding was delayed by 30 and 45 d from early May. Seeding rate had a small effect on grain yield. The response curve was very shallow peaking at approximately 1310 kg ha−1 at a seeding rate of 45 kg ha−1. Variation in grain yield tended to decrease as the seeding rate increased. There was a small increase in grain yield with the addition of nitrogen fertilizer. The response curve estimated a maximum yield of 1215 kg ha−1, which was obtained with a nitrogen rate of 78 kg ha−1. The majority of the increase was between 20 and 40 kg N ha−1, with a 2.3 kg ha−1 increase in grain yield for each kg of fertilizer N in that range of rates. There was a slight increase in grain yield as the nitrogen rate increased above 40 kg ha−1 but the variation in grain yield also increased reducing the incentive for growers to use N rates above 40 kg ha−1. Seeding date had a large effect on seed yield and could impact yield variability while seeding rate and nitrogen rate did not have a large effect on seed yield or yield variability.

Temporal and spatial variability of selected soil chemical properties were determined within and among three forested mid‐Cumberland Plateau landtypes at two locations. Major soils included Typic Fraguidults on uplands, Humic Hapludults on slopes, and Aquic Dystrochrepts in first‐order bottoms. Temporal soil variability was measured at monthly intervals for 25 consecutive months from samples taken from 0.5‐ by 0.5‐m subplots within two 2.5‐ by 2.5‐m plots on each landtype. Spatial variability samples were collected from similar, adjacent plots during a 1‐week period. A total of 300 spatial and 300 temporal observations were taken for each soil property in each of three soil horizons. Extractable Ca, Mg, and K, and pH in H 2 O and KCl were measured for A, AB, and Bt horizons, and total C was determined for A horizons. Magnitudes of variability were statistically compared using a procedure robust for nonnormally distributed data. Magnitudes of soil variability were significantly related to landtypes at the Bonferroni significance level. Only three of 16 soil variables differed in magnitudes of spatial vs. temporal variability. Variability on sites was of the order slopes > bottoms > uplands. Soil morphological features and parent materials appeared to strongly influence magnitudes of temporal and spatial variability.