Spatial Variability Of Crop Yield Research Articles

Estimation of Rice Yield Considering Heading Stage Using Satellite Imagery and Ground-Based Data in Indonesia

Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

INTRA-FIELD CROP YIELD VARIABILITY BY ASSIMILATING CUBESAT LAI IN THE APSIM CROP MODEL

Abstract. Predicting within-field crop yield early in the season can help address crop production challenges to improve farmers’ economic return. While yield prediction with remote sensing has been a research aim for years, it is only recently that observations with the suited spatial and temporal resolutions have become accessible to improve crop yield predictions.Here we developed a yield prediction framework that integrates daily high-resolution (3 m) CubeSat imagery into the APSIM crop model. The approach trains a regression model that correlates simulated yield to simulated leaf area index (LAI) from APSIM. That relationship is then employed to determine the optimum date at which the regression best predicts yield from the LAI. Additionally, our approach can forecast crop yield by utilizing a particle filter to assimilate CubeSat-based LAI in the model APSIM to generate yield maps at 3 m several weeks before the optimum regression date. Our method was evaluated for a rainfed site located in the US Corn belt, using a collection of spatially varying yield data. The proposed approach does not need in situ data to rain the regression, with outcomes reporting that even with a single assimilation step, accurate yield predictions were provided up to 21 days before the optimum regression date. The spatial variability of crop yield was reproduced fairly well, with a good correlation against in situ measurements (R2 = 0.73 and RMSE = 1.69), demonstrating that high-resolution yield predictions early in the season have great potential to meet and improve upon digital agricultural goals.

Weather-dependent relationships between topographic variables and yield of maize and soybean

Weather and topography are two important drivers of spatial variability in crop yield, but interactions between these two factors remain poorly understood. To elucidate how spatial yield variability shifts in response to precipitation, we collected data from published literature that examined the yield response of maize ( Zea mays L.) or soybean ( Glycine max (L.) Merr) to elevation, slope, planar curvature, or profile curvature. From these studies, we extracted correlations between yield and topographic variables for 86 site-years. We assessed the response of yield–topography correlations to the spring and total growing season precipitation of each site-year. Averaged across all site-years, maize yield was negatively correlated to elevation and planar curvature while soybean yield was negatively correlated to slope. For maize, the correlations between yield and elevation, slope, planar curvature, and profile curvature increased from negative to positive with increasing growing season precipitation, whereas for soybean the correlations between yield and elevation and between yield and slope became more negative with increasing growing season precipitation. Spring precipitation was a better predictor of yield–topography correlations than growing season precipitation for soybean but not for maize. We conclude that maize and soybean generally yield higher in low-elevation and low-slope landscape positions, respectively, but the yield–topography relationships vary with precipitation. • Maize and soybean yield response to topography was examined across 86 site-years. • Similar levels of spatial variability were observed in both maize and soybean. • Correlations between yield and topographic factors varied with precipitation. • Spring precipitation was an important predictor of soybean response to topography.

A Model-Based Estimation of Resource Use Efficiencies in Maize Production in Nigeria

Food security is an increasingly serious problem worldwide, and especially in sub-Saharan Africa. As land and resources are limited and environmental problems caused by agriculture are worsening, more efficient ways to use the resources available must be found. The objective of this study was to display the spatial variability in crop yield and resource use efficiencies across Nigeria and to give recommendations for improvement. Based on simulations from the crop model LINTUL5 we analyzed the influence of fertilizer application on the parameters Water Use Efficiency (WUE), Fertilizer Use Efficiency (FUE), and Radiation Use Efficiency (RUE) in maize. High spatial variability was observed, especially between the north and the south of the country. The highest potential for yield improvement was found in the south. While WUE and RUE increased with higher rates of fertilizer application, FUE decreased with higher rates. In order to improve these resource use efficiencies, we suggest optimizing management strategies, demand-oriented fertilizer application, and breeding for efficient traits.

Spatial variability of selected soil properties and its impact on the grain yield of oats (Avena sativa L.) in small fields

To investigate spatial variability in topsoil (0–20 cm) pH, available phosphorus (P), potassium (K), total nitrogen (N), and soil organic matter (SOM) of small fields (∼2 ha), and to determine the impact of soil heterogeneity on the spatial variability of crop yield two fields were cropped with spring oats and one with winter wheat under humid-temperate conditions. In the two oat fields, some of the measured soil properties (P, K) and the grain yield varied considerably, and strong spatial trends were recorded for most of the soil traits. In the third field, soil properties showed only a moderate spatial variation, and no spatial trends were found. The spatial distribution of SOM and total N in the topsoil had some influence on the spatial pattern of the oat grain yield in the field of Gränichen; however, spatial relationships between soil chemical properties and grain yield were rather weak in our study.

Sensing Technologies for Grain Crop Yield Monitoring Systems: A Review

Purpose: Yield monitoring systems are an essential component of precision agriculture. They indicate the spatial variability of crop yield in fields, and have become an important factor in modern harvesters. The objective of this paper was to review research trends related to yield monitoring sensors for grain crops. Methods: The literature was reviewed for research on the major sensing components of grain yield monitoring systems. These major components included grain flow sensors, moisture content sensors, and cutting width sensors. Sensors were classified by sensing principle and type, and their performance was also reviewed. Results: The main targeted harvesting grain crops were rice, wheat, corn, barley, and grain sorghum. Grain flow sensors were classified into mass flow and volume flow methods. Mass flow sensors were mounted primarily at the clean grain elevator head or under the grain tank, and volume flow sensors were mounted at the head or in the middle of the elevator. Mass flow methods used weighing, force impact, and radiometric approaches, some of which resulted in measurement error levels lower than 5% (R 2 = 0.99). Volume flow methods included paddle wheel type and optical type, and in the best cases produced error levels lower than 3%. Grain moisture content sensing was in many cases achieved using capacitive modules. In some cases, errors were lower than 1%. Cutting width was measured by ultrasonic distance sensors mounted at both sides of the header dividers, and the errors were in some cases lower than 5%. Conclusions: The design and fabrication of an integrated yield monitoring system for a target crop would be affected by the selection of a sensing approach, as well as the layout and mounting of the sensors. For accurate estimation of yield, signal processing and correction measures should be also implemented.

Using landscape typologies to model socioecological systems: Application to agriculture of the United States Gulf Coast

The complexity of socioecological systems (SES) has posed a persistent challenge to the development of methods for diagnostic and prognostic analyses of global change. We developed a high dimensional statistical framework where cluster analysis was used to characterize regional landscape typologies and those typologies are linked to the outcome of interest through regression modeling. For demonstration, we applied the framework to agroecosystem of the United States Gulf Coast to evaluate the determinants of spatial variability in crop yield. Regional biophysical typologies (BPT; integrated climate, soil, and topography clusters) and socioecological typologies (SET; BPT combined with socioeconomic clusters) were developed. The SET corn model (R2 = 0.89) outperformed the BPT corn model (R2 = 0.72) and a county fixed-effect model (R2 = 0.53), which reflects the socioeconomic influence over agricultural productivity. The SET model also showed similar predictive skill for soybean and cotton yield. Therefore impact analysis for agroecosystems can lead to incorrect conclusions if biophysical factors are not examined jointly with socioeconomic factors.

Mapping soil hydraulic conductivity and matric potential for water management of cranberry: Characterisation and spatial interpolation methods

Spatial interpolation methods are required for analysing the effects of soil hydraulic properties on irrigation management. This study was conducted to determine which interpolation methods are best suited to map these properties. During the summer of 2012 we mapped the spatial variability of soil physical properties, soil matric potential, water table depth and yield of two cranberry fields located near Quebec City, Canada. Three spatial interpolation methods, inverse distance weighting (IDW), thin plate splines (TPS) and kriging with external drift (KED), were compared by means of cross-validation. The best interpolation method for a given property was used to produce maps and perform HYDRUS 1D simulations for the purpose of irrigation management. Results show that even in highly constructed fields, such as for cranberries, spatial patterns of soil hydraulic properties exist. The TPS method was the best interpolation method based on the cross-validation analyses and generated maps. Spatial variability of crop yield showed a strong relationship with soil hydraulic properties and simulations suggest that irrigation can be reduced by 75% when accounting for the spatial variability of soil hydraulic properties.

Assessment of regional variability in crop yields with spatial autocorrelation: Banana farms and policy implications in Martinique

Agricultural research can support farmers and policy makers’ decisions by identifying the causes of spatial variability in crop yield at a regional level. In this paper, we propose a method that combines spatial autocorrelation measures and a farm network survey. This method is intended to describe the causes of spatial variability in crop yields, along with key crop management practices for reaching the best yields and the physical and socio-economic constraints of adopting these practices. This causal and hierarchical analysis of cropping system performance has the advantage of (1) preventing bias in the correlation between variables from the yield gap analysis and (2) formulating spatially targeted policies that are aimed at relaxing adoption constraints at the territorial level. After introducing the method and its different steps, we present the results of the assessment of the spatial variability in banana yields in Martinique (Caribbean). Our study has clearly shown that the planting stage is one of the most important aspects of banana production: allowing a long fallow period, plowing for soil preparation and using seedlings that are produced by tissue culture were associated with the best yields. However, several constraints limit their adoption by farmers at the regional level. The limiting factors were steep slopes, small farm size and low cash flow. We observed no relationship between pesticide use and yields. These study results finally permit the elaboration of spatially targeted policy recommendations to improve crop yields in a sustainable manner. It mainly consists in promoting and facilitating the adoption of good plantation practices for smallholders.

Spatial relationships between soil attributes and corn yield in no-tillage system

Soil properties play an important role in spatial variability of crop yield. However, a low spatial correlation has generally been observed between maps of crop yield and of soil properties. The objectives of the present investigation were to assess the spatial pattern variability of soil properties and of corn yield at the same sampling intensity, and evaluate its cause-and-effect relationships. The experimental site was structured in a grid of 100 referenced points, spaced at 10 m intervals along four parallel 250 m long rows spaced 4.5 m apart. Thus, points formed a rectangle containing four columns and 25 rows. Therefore, each sampling cell encompassed an area of 45 m² and consisted of five 10 m long crop rows, in which the referenced points represented the center. Samples were taken from the layers 0-0.1 m and 0.1-0.2 m. Soil physical and chemical properties were evaluated. Statistical analyses consisted of data description and geostatistics. The spatial dependence of corn yield and soil properties was confirmed. The hypothesis of this study was confirmed, i.e., when sampling the soil to determine the values of soil characteristics at similar to sampling intensity as for crop yield assessments, correlations between the spatial distribution of soil characteristics and crop yield were observed. The spatial distribution pattern of soil properties explained 65 % of the spatial distribution pattern of corn yield. The spatial distribution pattern of clay content and percentage of soil base saturation explained most of the spatial distribution pattern of corn yield.

Nitrogen and Water Uptake on Sloping Lands of Thailand

Cultivation in the tropical mountainous regions by using hedgerow systems as erosion control measures is recommended due to its effectiveness in reducing soil erosion and in supplying N. However, competition for nutrients and water between crops and hedges reduces crop performance and yield response. 13C stable isotope signature in combination with data on N and water availability and uptake in soil and plants framework was used to assess whether N or water function as a main driving force for spatial variability of crop yield along the alleys. The leaf δ13C values of maize were significantly (p<0.05) less depleted close to the hedges, suggesting that water stress was not the main driving force for spatial variability along the alleys. In the opposite, significant (p<0.05) N concentration depleted in maize leaf of plot with L. Leucocephala hedges, in particular at the row closed to hedgerow, in combination with significant (p<0.05) increase in δ13C values of leaf of the corresponding plot indicating the influence of N stress on poor maize performance and yield decrease. In addition, the significant (p<0.05) negative correlation between leaf δ13C values of maize, leaf N concentration and yield confirmed that N plays as a major role in crop decline towards hedgerows. Therefore, increasing amount of N fertilization to cropped area close to the hedgerows should be recommended for farmers, in order to encourage the acceptance of hedges system in tropical mountainous regions.

Delineation of Management Zones Using Soil Properties and a Multivariate Geostatistical Approach

Agricultural fields naturally have within-field variations that may cause spatial variability in crop yields. Delineating a field into a few management zones (MZ) is therefore a practical management approach for precision agriculture. In this study, some soil properties (clay content, organic matter content and water content measured at different dates) and a multivariate geostatistical approach were used to delineate management zones. The temporal stability of soil water content spatial patterns may be an additional approach in delineating management zones. The present study was conducted on a 120000 m2 durum wheat field in Southern Italy, where soil properties and soil water content were measured at different times over three crop seasons (2005-06, 2006-07, and 2007-08) at 100 locations. The multivariate spatial and temporal data were analysed by Factor Kriging Analysis (FKA). All simple and cross model variograms were calculated by fitting a linear model of coregionalization including the nugget effect and two spatial structures at shorter (70 m) and longer range (500 m). The application of factor kriging allowed us to identify one regionalized factor at longer scale and to partition the field from the point of view of temporal stability of soil water content, into contiguous zones to be submitted to site-specific management. The method showed a great flexibility to combine data from several sources in the delineation of management zones.

Day and night time sprinkler irrigated tomato: Irrigation performance and crop yield

The effect of day time vs. night time sprinkler irrigation on irrigation performance and tomato crop yield is assessed in this paper for the conditions of Tunisia. Field experiments were performed at the experimental station of Cherfech under two rectangular sprinkler spacings: 24 × 18 m and 18 × 18 m, denoted as plots M1 and M2, respectively. Results of performance evaluations indicate a significant effect of climatic and operation conditions on irrigation uniformity and wind drift and evaporation losses (WDEL). Experimental data were used to calibrate and validate a ballistic solid-set sprinkler irrigation simulation model and a soil-water-yield crop model. Based on the analysis of the main meteorological parameters during the irrigation season, the validated models were used to simulate night time irrigation (characterised by moderate wind speed and evaporative demand). Simulation results indicate that night time irrigation would greatly improve performance in comparison to day time operation: WDEL decreased from 24 to 7%, while irrigation uniformity increased from 50 to 64% in M1 and from 71 to 80% in M2. Simulated results showed that night time irrigation decreased relative yield losses (from 26 to 16% in M1 and from 11 to 3% in M2), as well as improving the spatial variability of crop yield (simulated yield CV in M2 decreased from 17 to 6%). Adoption of night irrigation in the study area will finally depend on local socioeconomic and water management constraints.

Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farm characteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently.

SIMPLE MODEL TO ESTIMATE DAILY LATERAL DRAINAGE

Lateral movement of soil solution could be a major cause for the spatial variability of crop yield within sloping landscapes. This study was an effort to develop a functional lateral downslope drainage model (LDDM) by expanding the Suleiman and Ritchie (2004) vertical drainage model. Within LDDM, the daily change of soil water content from a soil layer, due to lateral downslope drainage, was calculated by multiplying the drainable soil water [initial θ - drained upper limit θ (θ dul )] by hydraulic gradient and two coefficients. The two coefficients are C, which represents the soil hydraulic properties, and F, which accounts for the incoming soil water flow into a particular layer of the profile (Q;). To evaluate LDDM, soil water content profile and water table height were measured periodically at 15 locations along a transect along a sloping landscape during a lateral drainage cycle. The LDDM estimated lateral downslope soil water flow rate and the water table height reasonably well. The root mean square difference between estimated and measured daily lateral downslope soil water flow rate was 1.8 mm d -1 , and between estimated and measured cumulative weekly lateral downslope, soil water flow was 4.4 (first week), 16.3 (second week), and 8.7 (third week) mm.

Combining Field Surveys, Remote Sensing, and Regression Trees to Understand Yield Variations in an Irrigated Wheat Landscape

Improved understanding of the factors that limit crop yields in farmers’ fields will play an important role in increasing regional food production while minimizing environmental impacts. However, causes of spatial variability in crop yields are poorly known in many regions because of limited data availability and analysis methods. In this study, we assessed sources of between‐field wheat (Triticum aestivum L.) yield variability for two growing seasons in the Yaqui Valley, Mexico. Field surveys conducted in 2001 and 2003 provided data on management practices for 68 and 80 wheat fields throughout the Valley, respectively, while yields on these fields were estimated using concurrent Landsat satellite imagery. Management–yield relationships were analyzed with t tests, linear regression, and regression trees, all of which revealed significant but year‐dependent impacts of management on yields. In 2001, an unusually cool year that favored high yields, N fertilizer was the most important source of between‐field variability. In 2003, a warmer year with reduced irrigation water allocations, the timing of the first postplanting irrigation was found to be the most important control. Management explained at least 50% of spatial yield variability in both years. Regression tree models, which were able to capture important nonlinearities and interactions, were more appropriate for analyzing yield controls than traditional linear models. The results of this study indicate that adjustments in management can significantly improve wheat production in the Yaqui Valley but that the relevant controls change from year to year.

Measurement, scaling, and topographic analyses of spatial crop yield and soil water content

AbstractThe need to transfer information across a range of space–time scales (i.e. scaling) is coupled with the need to predict variables and processes of interest across landscapes (i.e. distributed simulation). Agricultural landscapes offer a unique set of problems and space–time data availability with the onset of satellite‐based positioning and crop yield monitoring. The present study addresses quantification of the spatial variability of rainfed crop yield and near‐surface soil water at farm field scales using two general methods: (1) geostatistical and fractal analyses; and (2) univariate linear regression using topographic attributes as explanatory variables. These methods are applied to 2 years of crop yield data from three fields in eastern Colorado, USA, and to soil‐water content (depth‐averaged over the top 30 cm) in one of these fields. Method 1 is useful for scaling each variable, and variogram shapes and their associated fractal dimensions of crop yield are related to those of topographic attributes. A new measure of fractal anisotropy is introduced and estimated from field data. Method 2 takes advantage of empirical and process knowledge of topographic controls on water movement and microenvironments. Topographic attributes, estimated from a digital elevation model at some scale (10 m by 10 m spacing here), help explain the spatial variability in crop yield. The topographic wetness index, for example, explained from 38 to 48% of the spatial variance in 1997 wheat yield. Soil water displays more random spatial variability, and its dynamic nature makes it difficult to predict in both space and time. Despite such variability, spatial structure is evident and can be approximated by simple fractals out to lag distances of about 450 m. In summary, these data and spatial analyses provide a basis and motivation for estimating the fractal behaviour, spatial statistics, and distributed patterns of crop yield from landscape topographic information. Copyright © 2004 John Wiley & Sons, Ltd.

Relationship among Crop Grain Yield, Topography, and Soil Electrical Conductivity Studied with Cross‐Correlograms

The characterization of factors causing spatial variability of crop yield is a necessary prerequisite for successful implementation of precision agriculture. However, the existing tools for a quantitative description of these factors and their relationship to crop yield lack effectiveness and completeness. This study proposed to use the area under an experimental cross‐correlogram as a single parameter to describe the spatial correlation between crop yield and topographical and soil variables. The developed parameter conveniently combines the information on the magnitude of the cross‐correlogram values, the correlation range, and the cross‐correlogram shape. We used the developed parameter to quantify the relationships between corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yield and soil apparent electrical conductivity (EC) and elevation in their spatial context. Variations in the strength and direction (positive vs. negative) of the relationship between yield and soil EC were found to be related to the amounts of precipitation observed early in the growing season. Crop yield was strongly and negatively related to EC in years with high March precipitation and positively or weakly negatively related to EC in years with low or moderate March precipitation. Results also indicated that in years with high March precipitation, the cumulative spatial correlation between soybean yield and EC was stronger than that between corn yield and EC. The range of significant spatial correlations between crop yield and elevation was related to the precipitation data as well as to the soil characteristics of the fields, such as soil organic matter content.

Wind effects on solid set sprinkler irrigation depth and yield of maize ( Zea mays )

A field experiment was performed to study the effect of the space and time variability of water application on maize (Zea mays) yield when irrigated by a solid set sprinkler system. A solid set sprinkler irrigation layout, typical of the new irrigation developments in the Ebro basin of Spain, was considered. Analyses were performed (1) to study the variability of the water application depth in each irrigation event and in the seasonal irrigation and (2) to relate the spatial variability in crop yield to the variability of the applied irrigation and to the soil physical properties. The results of this research showed that a significant part of the variability in the Christiansen coefficient of uniformity (CU), and wind drift and evaporation losses were explained by the wind speed alone. Seasonal irrigation uniformity (CU of 88%) was higher than the average uniformity of the individual irrigation events (CU of 80%). The uniformity of soil water recharge was lower than the irrigation uniformity, and the relationship between both variables was statistically significant. Results indicated that grain yield variability was partly dictated by the water deficit resulting from the non-uniformity of water distribution during the crop season. The spatial variability of irrigation water depth when the wind speed was higher than 2 m s−1 was correlated with the spatial variability of grain yield, indicating that a proper selection of the wind conditions is required in order to attain high yield in sprinkler-irrigated maize.

Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol) and a Red Yellow Latossol (Ultisol). Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.