Spatial Variation In Crop Yield Research Articles

Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate

As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.

Light availability, weed cover and crop yields in second generation of temperate tree-based intercropping systems

An experimental network representing a new generation of widely spaced tree-based intercropping (TBI) systems, which are more adapted to large-scale annual crops than narrowly spaced ones, was established in southern Quebec. Over two consecutive growing seasons (2016-2017), we determined effects of these TBI systems on percent total light transmittance (PTLT), soil moisture, weed cover and crop yield. We studied six experimental sites that included different annual crops (maize, soybean, wheat, black bean) or forage crops. Age of TBI systems varied between 3 and 22 years, and spacing between tree rows ranged from 25 to 90 m. Variables were measured in control (non-tree) farm plots and in TBI alleys at four distances from the row of trees. Spatial variation in crop yield in TBI alleys was crop-specific. Yield reductions for maize, soybean and black bean were observed at the closest distance to the tree row (½H, where H = tree height). These yield reductions were associated with a decrease in PTLT or an increase in weed cover. Soil moisture changed little with distance from the row of trees and no significant relationship between crop yield and soil moisture was measured, which suggested that competition for water between trees and crops was marginal. Our results indicate that forage crops are less sensitive than annual crops to changes in environmental conditions that were incurred by TBI systems. Overall, the second-generation TBI systems under study exerted neutral net effects on integrated crop yields (i.e., all distances combined), except for black bean in 2016. Second-generation TBI agricultural yields appear to be more promising than those obtained in first-generation TBI systems.

Adapatation of Sowing Date for Improving Sorghum Yield in Rainfed Areas in Sudan Using AQUACROP Model

Due to the rapid growth in world population, the pressure on water resources to feed the growing population is increasing. The Nile water share of Sudan is almost exploited; and agricultural production by rained water is threatened by the pressure of climate change. It is inevitable that the production per unit water consumed, the water productivity, must be increased to meet this challenge. This research therefore focuses on the benchmarking of physical water productivity in rain fed areas and gaining a better understanding of the temporal and spatial variations and the scope for possible improvement. A review of the available records and sources that provide measurements of crop-water productivity was consulted to assess plausible ranges of water productivity levels for rain fed Sorghum crop and to provide a first explanation for the differences that are found using AQUACROP model. As such this study may be considered as crucial step was to establish a water productivity database for the rain fed sorghum crop in the country. Sorghum (Sorghum bicolor (L.) Moench) which is the most important cereal crop in Sudan has been constrained by the detrimental effect of drought which has often caused food shortages. Almost 90% of the total sorghum cropped area is rain-fed, and 60% of that is in drought prone soil conditions. Spatial information on water use, crop production and water productivity will play a vital role for water managers to assess where scarce water resources are wasted and where in a given region the water productivity can be improved. Hence, a methodology has been developed in this study to quantify spatial variation of crop yield, evapotranspiration and water productivity using the AQUACROP model in five stations. The AQUACROP model is used to investigate optimum sowing date that result in maximization of grain yield.Benchmarking of rain fed Sorghum actual and potential grain efficiency in different agro-climate zones was made for the year 1979 to 2013. AQUACROP model was applied at five locations (Gedaref, Damazin, Dalang, El Fashir, and El Obyied) each representing an agro-climate zone. Causes of poor yield performance were investigated and consequently measures needed to improve performance were identified. The study indicates that increase in sorghum yields under historical climate conditions in the different studied stations is possible when early sowing is used and initial rain showers are utilized, yield decrease by 43% when sowing date is delayed from July 15 (the recommended date) to August 1. Stations with high rain fall (Damazin, Gadaref and Dalang) show little variations in inter-annual yields but with a tendency towards high yields, 3536, 3741, 3737 kg/fed for the above stations respectively compared to 2266 and 1086 kg/fed for El Obyied and El Fashir respectively at 15 June. The obtained WUE is lower in the driest regions (El Fashir, and El Obyied) and higher for those of high rain fall. To aid decision makers and crop growers in rain fed areas a set of recommendations for policy making and for future research were identified.

Whole farm implications on the application of variable rate technology to every cropped field

In the last 10 years precision agriculture has evolved and it is now possible to manage cropping inputs at the sub field scale. However, the expected return generated by these management practices varies with the amount and predictability of the spatial variation in crop yield across the field and may vary from field to field across the farm. We analysed the spatial variation of crop yield from 20 fields totalling 2832 ha from a 5200 ha farm in Western Australia at the field and the sub field scale to determine the economic value of introducing variable rate management (VRT) strategies to fields across the entire farm. In this case study, 6 out of 20 fields generated an additional economic payoff of AU$15/ha when managed with VRT over uniform management. Sensitivity analysis found that the starting levels of soil fertility were an important driver of the payoff for managing a field with VRT. If starting soil fertility was low and uniform across the field, then no fields generated an economic return of more than AU$7.00/ha when managed with VRT. Other factors, including commodity price, fertiliser price, amount of variation in crop yield within the field moderately increased the value of adopting VRT across fields within farm the value of VRT increased if fertiliser budgets were constrained, particularly if the yield distribution of the field was negatively skewed. While VRT does add value to many fields across the farm, the gains are small and the technology must be implemented cheaply for it to be viable.

Crop Yield Forecasting from Remotely Sensed Aerial Images with Self-Organizing Maps

Crop yield varies spatially and temporally due to several limiting factors, including soil type, nutrient availability, water availability, weather, and crop varieties. It is difficult to quantify the severity of these limiting factors on crop yield as well as the spatial variation of crop yield before harvest. Remotely sensed aerial images can be used to improve estimates of mid-season crop yields. However, the success of such approaches is typically dependent on the image classification technique used. In this article, we develop an automated self-organizing map (SOM) neural image classification technique and evaluate it with respect to the widely used ISODATA clustering method using visual assessment of preservation of image texture. We also develop hybrid SOM-back-propagation neural network (BPNN) models to predict crop yield from the Oakes Irrigation Test Area (OITA) research sites at Oakes, North Dakota. We used the SOM image classification technique to classify the composite visible and near-infrared (NIR) band aerial images from several parts of the OITA research site. SOM cluster centroids from individual grid images were used as inputs to the BPNN yield prediction models created for the years 1997, 1998, 1999, 2000, and 2001. The models using individual red, green, and NIR band digital data provided average corn yield prediction accuracies of more than 80% for every year, except for the NIR model in 1998 (45%). Individual year models were validated with individual band training and testing datasets from the other four years. Models using the green band dataset provided better validation results. Average corn yield prediction accuracies ranged from 55% to a maximum of 96%. Average corn yield prediction accuracies of validated models using the red band and NIR band datasets ranged from 33% to a maximum of 94% and from 36% to a maximum of 95%, respectively. This article establishes the use of hybrid SOM-BPNN modeling techniques to predict corn yield from aerial images at mid-crop season.

Airborne Hyperspectral Imagery for Mapping Crop Yield Variability

AbstractInformation concerning the spatial variation in crop yield has become necessary for site‐specific crop management. Traditional satellite imagery has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. However, this type of imagery has limited use for assessing within‐field yield variability because of its coarse spatial resolution. Therefore, high‐resolution airborne multispectral and hyperspectral imagery has been used for this purpose. This study presents an overview on the use of remote sensing imagery for mapping crop yield variability and illustrates how airborne hyperspectral imagery can be used for crop yield estimation based on the work conducted at the US Department of Agriculture’s Kika de la Garza Subtropical Agricultural Research Center at Weslaco, Texas. Some of the challenges and considerations on the use of hyperspectral imagery for yield mapping are discussed.

Capturing the in-field spatial - temporal dynamic of yield variation

Many researchers have predicted that within-field spatial variation in crop yield could be exploited for economic benefit. However, the spatial variation of yield is influenced by season and is often temporally unstable. Parts of a field may yield well relative to the remainder of the field in one season and poorly in another, suggesting that regions in the field vary in their response to season type. We evaluate the capacity of two analytical techniques, the regression on the field mean and the regression on growing-season rainfall, to capture the variation in responsiveness of yield variation across a field. We applied these indices to a commercial 134-ha field that had been sown to wheat, Triticum aestivum cv. Calingiri, in 1996, 1999, 2001, 2003, and 2005. The slope from the regression on field mean was variable with a mean of 1 and standard deviation of 0.67. The technique successfully identified regions that were responsive and unresponsive to variations in the cropping environment. In contrast, the average slope derived from the regression on growing-season rainfall was just –0.003 ± 0.003 t/ha.mm of growing-season rainfall. This approach failed to capture the spatial–temporal dynamic of yield variation, and implies that the overall cropping environment was poorly characterised by growing-season rainfall. Crop yields, derived from 10 soils in this field, were simulated from 1900 to 2006. The analytical techniques were applied to these simulated yields and revealed that the spatial–temporal dynamic observed in the field is partially explained by the interactions between soil type and climate. In addition, the spatial–temporal dynamic is best captured when mean field yields vary temporally by more than 1.2 t/ha if the assessments are made with 5 years of data. We further discuss the application and interpretation of these indices and the role they play in identifying soils that are responsive to season type.

SEBAL for detecting spatial variation of water productivity and scope for improvement in eight irrigated wheat systems

A methodology has been developed to quantify spatial variation of crop yield, evapotranspiration (ET) and water productivity (WP ET) using the SEBAL algorithm and high and low resolution satellite images. SEBAL-based ET estimates were validated over an irrigated, wheat dominated area in the Yaqui Valley, Mexico and proved to be accurate (8.8% difference for 110 days). Estimated average wheat yields in Yaqui Valley of 5.5 t ha −1 were well within the range of measured yields reported in the literature. Measured wheat yields in 24 farmers’ fields in Sirsa district, India, were 0.4 t ha −1 higher than SEBAL estimated wheat yields. Area average WP ET in the Yaqui Valley was 1.37 kg m −3 and could be considered to be high as compared to other irrigated systems around the world where the same methodology was applied. A higher average WP ET was found in Egypt's Nile Delta (1.52 kg m −3), Kings County (CA), USA (1.44 kg m −3) and in Oldambt, The Netherlands (1.39 kg m −3). The spatial variability of WP ET within low productivity systems (CV = 0.33) is higher than in high productivity systems (CV = 0.05) because water supply in the former case is uncertain and farming conditions are sub-optimal. The high CV found in areas with low WP ET indicates that there is considerable scope for improvement. The average scope for improvement in eight systems was 14%, indicating that 14% ET reduction can be achieved while maintaining the same yield. It is concluded that the proposed methodology is accurate and that better knowledge of the spatial variation of WP ET provides valuable information for achieving local water conservation practices in irrigated wheat.

Can Landform Stratification Improve Our Understanding of Crop Yield Variability?

Farmers account for yield and soil variability to optimize their production under mainly economic considerations using the technology of precision farming. Therefore, understanding of the spatial variation of crop yield and crop yield development within arable fields is important for spatially variable management. Our aim was to classify landform units based on a digital elevation model, and to identify their impact on biomass development. Yield components were measured by harvesting spring barley (Hordeum vulgare, L.) in 1999, and winter rye (Secale cereale, L.) in 2000 and 2001, respectively, at 192 sampling points in a field in Saxony, Germany. The field was stratified into four landform units, i.e., shoulder, backslope, footslope and level. At each landform unit, a characteristic yield development could be observed. Spring barley grain yields were highest at the level positions with 6.7 t ha−1 and approximately 0.15 t ha−1 below that at shoulder and footslope positions in 1999. In 2000, winter rye harvest exhibited a reduction at backslope positions of around 0.2 t ha−1 as compared to the highest yield obtained again at level positions with 11.1 t ha−1. The distribution of winter rye grain yield across the different landforms was completely different in 2001 from that observed in 2000. Winter rye showed the highest yields at shoulder positions with 11.1 t ha−1, followed by the level position with 0.5 t ha−1 less grain yield. Different developments throughout the years were assumed to be due to soil water and meteorological conditions, as well as management history. Generally, crop yield differences of up to 0.7 t ha−1 were found between landform elements with appropriate consideration of the respective seasonal weather conditions. Landform analysis proved to be helpful in explaining variation in grain yield within the field between different years.

Engineering precision into variable biological systems

Technology change stimulates the expectations of society and seeks to satisfy its demands. Continuing advances in electronics and information technology over the last 20 years have responded to the challenge posed by the environmental impacts of agricultural production systems. Future agriculture will require precision techniques to assemble information and achieve increasingly precise and responsive management practices in order to reduce wasteful inputs, and meet the social and economic pressures for safe high quality food at lower cost. Biosystems engineering, an interdisciplinary science linking biology, physics, engineering and mathematics, can provide understanding and then innovative precision solutions for agriculture. Public and industry investment will be needed to achieve the necessary goals of lower environmental impact, and precision techniques will make a signi. cant contribution. Worldwide, the availability of tools that allow accurate control of inputs and traceable management of food production will contribute to sustainable food production for all. The vision is of systems that utilise sensing methods and mathematical models of the biological process, and link them through to control algorithms that realise practical benefits. Tools to measure spatial variation in crop yield will draw on statistical models to interpret complex variation and provide robust information to justify variable input management. Machine vision will interpret complex natural scenes, leading to real-time control, and biosensors will be incorporated into automated systems for sensing animal fertility status, leading to automatic monitoring of animal health and welfare.

Exploring the spatial relations between cereal yield and soil chemical properties and the implications for sampling

Abstract. In general, agricultural management has focused on differences between fields or on the gross differences within them. Recent developments in agricultural technology, yield mapping, Global Positioning Systems and variable rate applications, have made it possible to consider managing the considerable variation in soil and other properties within fields. This system is known as precision agriculture. More precise management of fields depends on a better understanding of the factors that affect crop input decisions. This paper examines the spatial variation in crop yield, soil nutrient status and soil pH within two agricultural fields using geostatistics. The observed properties vary considerably within each field. The relation between yield and the measured soil properties appears to be weak in general. However, the range of spatial correlation for yield, shown by the variogram, is similar to that of the soil chemical properties. In addition the latter changed little over two years. This suggests that information on the scale of variation of soil chemical properties can be derived from yield maps, which can also be used as a guide to a suitable sampling interval for soil properties.

An empirical method for describing the joint effects of environmental and other variables on crop yield

SummaryThere is considerable interest in developing understanding of the spatial variation of crop yield within fields in relation to the spatial variation of environmental factors. Such understanding might allow inputs to crops to be used more efficiently by spatially variable application. The joint action of factors on crop yield (whether controlled treatments or environmental variables) may be additive, interactive or potentially limiting in the sense of von Liebig's “Law of the Minimum”. The nature of the joint action of an input and environmental factors has profound implications for the possibility of improving the efficiency of use of the input by spatially variable application. Conventional methods for analysing data on such factors and yield (e.g. multiple regression) make specific assumptions compatible only with a narrow range of permissible models. A procedure is proposed and demonstrated in this paper for the exploratory analysis of data from experiments and/or crop surveys in order to describe the joint action of important factors on crop yield. The procedure could be applied to specific experiments in order to indicate whether there is any scope for optimising rates of an input with respect to one or more spatially variable factors, and for indicating whether the relationship between the local optimum rate of the input and values of these factors is simple (factors act additively or in accordance with the Law of the Minimum) or complex (factors interact).

Classification as a first step in the interpretation of temporal and spatial variation of crop yield

Summary.Automated pattern recognition by multivariate clustering is proposed as a tool for interpreting the temporal and spatial variation of crop yield. A trial showed that some general patterns of season‐to‐season variation could be identified and related to soil variability. Such a procedure would be a useful first step in the investigation of sources of yield variation, leading to interpretation and (possibly) spatial variation of inputs.

The Control of Errors in Momentary Yield Data from Combine Harvesters

The installation of grain flow sensors and positioning equipment on combine harvesters has provided the means to identify spatial variation in crop yield. Changes in the rate of grain flow at a grain flow sensor sometimes do not reflect changes in crop intake giving rise to erroneous momentary yield data. This study examines the effect of data screening on the statistical quality of momentary yield data. A data screen based on the modelling of the pattern of grain flow at the start of harvest runs is the focus of particular attention. Momentary yield data were subjected to geostatistical analysis following error screening. Three screening methods, with the most rigorous based on a model of the grain flow in the first 60 s following the lowering of the cutterbar, were applied to the data. The average of the momentary yield data passing this screen was similar to the average yield calculated from the total grain quantity and the area harvested. Geostatistical analysis showed that this screen also gave the highest quality data. The effects of different sampling intensities from 1-10 s on the statistical quality of the data were minor. Geostatistical techniques for examining and interpolating momentary yield data are outlined and proposals for the automatic control of yield data logging are presented. With this approach, the data logging system can be programmed to delete data on the basis of their association with harvester operating patterns that cause these errors.