Identifying Advantages and Disadvantages of Variable Rate Irrigation – An Updated Review

Variable rate irrigation (VRI) sprinklers on mechanical move irrigation systems (center pivot or lateral move) have been commercially available since 2004. Although the number of VRI, zone or individual sprinkler, systems adopted to date is lower than expected there is a continued interest to harness this technology, especially when climate variability, regulatory nutrient management, water conservation policies, and declining water for agriculture compound the challenges involved for irrigated crop production. This article reviews the potential advantages and potential disadvantages of VRI technology for moving sprinklers, provides updated examples on such aspects, suggests a protocol for designing and implementing VRI technology and reports on the recent advancements. The advantages of VRI technology are demonstrated in the areas of agronomic improvement, greater economic returns, environmental protection and risk management, while the main drawbacks to VRI technology include the complexity to successfully implement the technology and the lack of evidence that it assures better performance in net profit or water savings. Although advances have been made in VRI technologies, its penetration into the market will continue to depend on tangible and perceived benefits by producers

METHODS OF DELINEATING IRRIGATION MANAGEMENT ZONES FOR VARIABLE RATE IRRIGATION

Uniform rate irrigation on variable soil landscapes can cause spatial differences in plant available water, which leads to inconsistent crop yields as well as the inefficient use of water resources and irrigation infrastructure. Variable rate irrigation has the potential to increase water use efficiency and reduce spatial variability in plant available water by customizing irrigation applications across variable soil landscapes. Variable rate irrigation is implemented by delineating a field into management zones with relatively homogeneous available water holding capacity. These have traditionally been delineated using soil apparent electrical conductivity (ECa) mapping; however, concerns with interference from soil salinity and laborious data acquisition has created a demand for new ways of identifying spatial variability in available water-holding capacity (AWC). One emerging approach for this is based on interactions of plant stress response to soil moisture conditions inferred using remote sensing techniques. This thesis introduced and field-tested two methods of delineating irrigation management zones that utilize remote sensing indices to measure plant response during a drydown scenario. The indices examined were apparent canopy temperature and normalized difference vegetation index (NDVI). The traditional (ECa) and emerging (apparent canopy temperature and NDVI) zone delineation methods were compared by testing the ability of these methods to identify spatial variability in AWC between 48 sample locations in a 16-ha irrigated field in Outlook, Saskatchewan. Available water holding capacity was quantified at the 48-sampling locations by determining the water retention characteristic of soil horizons that differ in texture using the pressure plate method. Soil apparent electrical conductivity (ECa) was acquired via EM38-Mk2 survey on bare soil. Apparent canopy temperature and NDVI remote sensing data were acquired via unmanned aerial vehicle (UAV) during early and late stages of a drydown scenario on an established wheat crop. As the field dried, spatial variability in plant available water became apparent between areas in the field with low and high AWC, which helped to develop a relationship between the plant response methods and AWC. The apparent canopy temperature method was found to outperform the traditional zone delineation methods under both early and late drydown conditions, whereas the NDVI method was only able to outperform ECa under late drydown conditions. This is a substantial limitation for NDVI because the late drydown conditions caused crop damage in areas of the field with low AWC. The ECa method was found to accurately identify spatial variability in AWC at the field site; however, this method performed poorly in salinity affected soils. Apparent canopy temperature has the potential to be a suitable replacement for traditional zone delineation methods, as this method was able to delineate accurate management zones under minor drydown conditions, which did not cause apparent crop damage in wheat. However, the utility of this method can be diminished by crop damage and error caused by variable cloud cover during data acquisition. The practical considerations and abilities of each method to identify spatial variability in AWC are key factors for determining the most practical method or combination of methods to utilize for delineating management zones for variable rate irrigation

Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand

Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact

Maximizing the relationship of yield to site-specific management zones with object-oriented segmentation of hyperspectral images

Quick and low cost delineation of site-specific management zones (SSMZ) would improve applications of precision agriculture. In this study, a new method for delineating SSMZ using object-oriented segmentation of airborne imagery was demonstrated. Three remote sensing domains—spectral, spatial, and temporal- are exploited to improve the SSMZ relationship to yield. Common vegetation indices (VI), and first and second derivatives ([Formula: see text], [Formula: see text]) from twelve airborne hyperspectral images of a cotton field for one season [Formula: see text] were used as input layers for object-oriented segmentation. The optimal combination of VI, SSMZ size and crop phenological stage were used as input variables for SSMZ delineation, determined by maximizing the correlation to segmented yield monitor maps. Combining narrow band vegetation indices and object-oriented segmentation provided higher correlation between VI and yield at SSMZ scale than that at pixel scale by reducing multi-resource data noise. VI performance varied during the cotton growing season, providing better SSMZ delineation at the beginning and middle of the season (days after planting (DAP) 66–143).The optimal scale determined for SSMZ delineation was approximately 240 polygons for the study field, but the method also provided flexibility enabling the setting of practical scales for a given field. For a defined scale, the optimal single phenological stage for the study field was near July 11 (DAP 87) early in the growing season. SSMZs determined from multispectral VIs at a single stage were also satisfactory; compared to hyperspectral indices, temporal resolution of multi-spectral data seems more important for SSMZ delineation

Using Spatial Analysis to Study the Values of Variable Rate Technology and Information

We present a review of the last few years' literature on the economic feasibility of variable rate technology in agriculture. Much of the research on this topic has involved the estimation of site-specific yield response functions. Data used for such estimations most often inherently lend themselves to spatial analysis. We discuss the different types of spatial analyses that may be appropriate in estimating various types yield response functions. Then, we present a taxonomy for the discussion of the economics of precision agriculture technology and information. We argue that precision agriculture technology and information must be studied together since they are by nature economic complements. We contend that longer-term, multi-location agronomic experiments are needed for the estimation of ex ante optimal variable input rates and the expected profitability of variable rate technology and information gathering. We use our taxonomy to review the literature and its results with consistency and rigor.precision agriculture, spatial econometrics, variable rate technology, Research and Development/Tech Change/Emerging Technologies, C31, O33, Q16,

Economics of Management Zone Delineation in Cotton Precision Agriculture

This paper develops a management zone delineation procedure based on a spatial statistics approach and evaluates its economic impact for the case of Texas cotton production. With the use of an optimization model that utilizes a yield response function estimated through spatial econometric methods, we found that applying variable N rates based on the management zones delineated would result in higher cotton yields and higher net returns, above Nitrogen cost, relative to uniformly applying a single N rate for the whole field. In addition, a variable rate N application using the delineated management zones produced higher net returns, above Nitrogen cost, relative to a variable N rate system where the zones are based solely on landscape position. This is indicative of the potential economic value of using a spatial statistics approach to management zone delineation.Management Zones, Exploratory Spatial Data Analysis, Site-Specific Nitrogen Management, Cotton Precision Agriculture., Crop Production/Industries, Q1, Q16,

Agriculture field characterization using GIS software and scanned color infrared aerial photographs

Non-Peer ReviewedThis paper addresses the potential of a color infrared aerial photograph to provide spatially distributed information for site specific management. In this process digitized color infrared aerial photographs were used to extract vegetation index information. In addition, important crop and soil information were also collected using a grid sampling technique. Crop and soil information contributing most to explain variability were determined and used in further analysis. Grain yield data obtained using combine sampling were noted along with the coordinate information of the sample points. Locational information were collected using GPS. Kriged surface were generated using soil and crop point sample information. Point information were extracted from each kriged surface using centroid of uniformly spaced grid (15 m cell). Fuzzy k-means with extragrades algorithms were used to delineate potential within-field management units based on soil and crop information and vegetation index separately. Then “goodness” of potential management zones generated using within zone variability of grain yield. Ideal number of zones were determined using the decrease in total within-zone variance. Finally, management zones determined using crop and soil information and vegetation index information were compared for similarity. The methodology is fast, can be easily automated in commercially available GIS software and has considerable advantages when comparing to other methods for delineating within-field management zones

A Novel Approach for Management Zone Delineation by Classifying Spatial Multivariate Data and Analyzing Maps of Crop Yield

Precision farming has been playing a distinguished role over last few years. It encompasses the techniques of Data Mining and Information Technology into agricultural process. The acute task in classic agriculture is fertilization, which makes minerals available for crops. Site specific methods result in imbalanced management within fields which affects the crop yield. Treating the whole field as uniform area is merely heedless as it forces the farmers to use costly resources like fertilizers, pesticides etc., at greater expenses. As the field is heterogeneous, the critical task is to identify which part of the field should be considered and the percentage of fertilizer or pesticide required. In order to increase the yield productivity, concept of Management Zone Delineation (MZD) has to be adopted, which divides the agricultural field into homogeneous subfields, or zones based on the soil parameters. Precision Agriculture focuses on the utilization of Management zones (MZs). In this paper, we have collected huge data of Davanagere agricultural jurisdiction during standard farming operations which reflects the heterogeneity of agricultural field. We base our work on a new Data Mining technique called Kriging, which interpolates soil sample values for the specific region, which in turn helps in converting heterogeneous zones to homogeneous subfields

A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

Management Zones Delineation through Clustering Techniques Based on Soils Traits, NDVI Data, and Multiple Year Crop Yields

Availability of georeferenced yield data involving different crops over years, and their use in future crop management, are a subject of growing debate. In a 9 hectare field in Northern Italy, seven years of yield data, including wheat (3 years), maize for biomass (2 years), sunflower, and sorghum, and comprising remote (Landsat) normalized difference vegetation index (NDVI) data during central crop stages, and soil analysis (grid sampling), were subjected to geostatistical analysis (semi-variogram fitting), spatial mapping (simple kriging), and Pearson’s correlation of interpolated data at the same resolution (30 m) as actual NDVI values. Management Zone Analyst software indicated two management zones as the optimum zone number in multiple (7 year) standardized yield data. Three soil traits (clay content, total limestone, total nitrogen) and five dates within the NDVI dataset (acquired in different years) were shown to be best correlated with multiple-and single-year yield data, respectively. These eight parameters were normalized and combined into a two-zone multiple soil and NDVI map to be compared with the two-zone multiple yield map. This resulted in 83% pixel agreement in the high and low zone (89 and 10 respective pixels in the soil and NDVI map; 73 and 26 respective pixels in the yield map) between the two maps. The good agreement, which is due to data buffering across different years and crop types, is a good premise for differential management of the soil-and NDVI-based two zones in future cropping seasons