Site-specific Management Units Research Articles

Uniformity and spatial variability of soil moisture and irrigation distribution on natural turfgrass sports fields

Emerging technology that couples spatial irrigation distribution data with spatial plant and soil property data may provide a more robust assessment of irrigation system performance than conventional methods. Research was conducted on native soil and sand capped sports fields to (1) compare the lower quarter distribution uniformity (DU_lq) and spatial variability of soil moisture (volumetric water content [VWC]) and irrigation distribution (using the catch can method); (2) investigate the influence of catch can amount, soil compaction (penetration resistance), and turfgrass vigor (normalized difference vegetation index [NDVI]) on VWC; and (3) delineate and compare site-specific management units (SSMUs) for VWC and catch can amount to generate more informed irrigation-based management decisions. Volumetric water content DU_lq was much higher than catch can DU_lq on the native soil field, but the DU_lqs were similar on the sand capped field. Spatial maps of VWC and catch can amount indicated that the spatial variability of VWC was not reflected in the spatial variability of catch can amount on either field. Penetration resistance and NDVI were significant at predicting VWC on the native soil field (<i>p</i> &lt; 0.001 and <i>p</i> &lt; 0.01, respectively), and their spatial maps resembled the VWC map. Only catch can amount was significant at predicting VWC on the sand capped field (<i>p</i> &lt; 0.001), but their spatial maps were dissimilar. Volumetric water content SSMUs would be useful for site-specific irrigation, soil moisture sensor placement, or aerification. Catch can amount SSMUs would be useful to visually identify and then correct deficiencies in an irrigation system9s water distribution. Implementation of suggested management strategies may result in the conservation of water by improving overall irrigation efficiency.

Spatiotemporal variation of site-specific management units on natural turfgrass sports fields during dry down

Site-specific management units (SSMUs) are fundamental for the implementation of Precision Turfgrass Management. Short-term spatiotemporal variations of soil compaction and turfgrass vigor may be dynamic during a dry down period on natural turfgrass sports fields. This is due to the inverse relationship between soil compaction and soil moisture/drought stress, which may impact SSMU delineation and identification of site-specific deficient areas within a field. The spatiotemporal change of soil moisture, soil compaction, and turfgrass vigor SSMUs [as measured by volumetric water content (VWC), penetration resistance, and normalized difference vegetative index (NDVI)] were evaluated three times during a dry down from rainfall on native soil and sand capped natural turfgrass sports fields. The relationship of penetration resistance and NDVI with VWC was strongest and only significant on the native soil field during the dry down period. In general, as the fields dried, the magnitude of VWC SSMUs and NDVI SSMUs decreased, while the magnitude of penetration resistance SSMUs increased. This phenomenon was more drastic on the native soil field. Significant changes in spatial distributions were observed for VWC SSMUs and penetration resistance SSMUs on the native soil field; however, minimal changes were reported on the sand capped field. The spatial distributions of NDVI SSMUs were minimal on both fields. It is concluded that short-term spatiotemporal variations of SSMUs on sports fields during a dry down can be significant and considerations should be made prior to sampling based on the objective.

Delineation of site-specific management units for operational applications using the topographic position index in La Pampa, Argentina

In this work we propose the use of the topographic position index (TPI), which takes into account the local topography for a given neighborhood, to delineate management units (MU) for site-specific systems. This study was performed in the province of La Pampa, in central Argentina, an area with sandy soils where the main limiting condition for crops is soil moisture. Usually, multi-annual yield maps are used for the delineation of MU. However, those are strongly influenced by issues that could be related to un-calibrated data and previous agronomical practices. Thus, there was a need for a methodology based on stable and unbiased parameters. The methodology was developed for a representative 100ha field. The average size and orientation of the topographic structures were characterized applying the autocorrelation function on the topographic data, which was then used to determine an optimum neighborhood size for the TPI. TPI performed better than the topographic map to characterize the variability of the field. The correlation between yield and TPI was higher (r=0.74) than that between yield and topography (r=0.54). The resulting management units were delineated using an unsupervised classification approach on the TPI maps. From the confusion matrices, the overall accuracy was higher for the TPI derived maps than for the topography derived maps (62% against 47%) when compared to a yield map used as reference. We estimate that this methodology could be used for operational applications, the only requirement being topographic data for a given field, since it is simple, the algorithms used are unbiased and it could be performed using free software.

Mapping Soil Water Capacity Through EMI Survey to Delineate Site-Specific Management Units Within an Irrigated Field

An accurate and high-resolution mapping of soil properties allows optimizing the management of irrigation and fertilization at field scale by applying variable amounts of water and nutrients. Site-specific management (SSM) is fundamental to improve crop yield and to use resources more efficiently, improving environmental sustainability. Adoption of site-specific management practices requires the delineation in the field of subregions with similar soil properties affecting yield (site-specific management units (SSMU)). It is common practice to characterize the spatial variability of soil properties through electromagnetic induction (EMI) surveys to obtain soil electrical conductivity (EC) maps that can be used to delineate SSMU. The objectives of this work, carried out over a uniformly drip-irrigated and fertilized maize, were to (i) delineate SSMU from EC maps; (ii) compare the SSMU inferred from measurements with two different EMI sensors; (iii) map the soil-available water-holding capacity (AWC) from EC maps through a regression model between EC and measured AWC; and (iv) evaluate significant differences of crop yield among the SSMU. The EC maps at increasing depths were processed through principal component analysis, and three SSMU were delineated for both EMI sensors using the Management Zone Analyst software. The significant difference in crop yield across the three SSMU, tested through the analysis of variance, suggested that AWC was the main limiting factor in crop yield. This result highlights the importance of a variable-rate irrigation based on SSMU, which could be a solution to save water and increase crop yield.

Delineation of site-specific management units in a saline region at the Venice Lagoon margin, Italy, using soil reflectance and apparent electrical conductivity

Site-specific crop management utilizes site-specific management units (SSMUs) to apply inputs when, where, and in the amount needed to increase food productivity, optimize resource utilization, increase profitability, and reduce detrimental environmental impacts. It is the objective of this study to demonstrate the delineation of SSMUs using geospatial apparent soil electrical conductivity (ECa) and bare-soil reflectance measurements. The study site was a 21-ha field at the southern margin of the Venice Lagoon, Italy, which is known to have considerable spatial variability of soil properties influencing crop yield. Maize (Zea mais L.) yield maps from 2010 and 2011 showed high spatial heterogeneity primarily due to variation in soil-related factors. Approximately 53% of the spatial variation in maize yield was successfully modeled according to the variability of four soil properties: salinity, texture, organic carbon content, and bulk density. The spatial variability of these soil properties was characterized by the combined use of intensive geospatial ECa measurements and bare-soil normalized difference vegetation index (NDVI) survey data. On the basis of the relationships with these soil properties, ECa and NDVI were used to divide the field into five SSMUs using fuzzy c-means clustering: one homogeneous with optimal maize yield, one unit affected by high soil salinity, one characterized by very coarse texture (i.e., sandy paleochannels), and two zones with both soil salinity and high organic carbon content. Yield monitoring maps provide valuable spatial information, but do not provide reasons for the variation in yield. However, even in cases where measurements like ECa and bare-soil NDVI are not directly correlated to maize yield, their combined use can help classify the soil according to its fertility. The identification of areas where soil properties are fairly homogeneous can help managing diverse soil-related issues optimizing resource use, lowering costs, and increasing soil quality.

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor. Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.

Apparent soil electrical conductivity measurements in agriculture

The field-scale application of apparent soil electrical conductivity (EC a) to agriculture has its origin in the measurement of soil salinity, which is an arid-zone problem associated with irrigated agricultural land and with areas having shallow water tables. Apparent soil electrical conductivity is influenced by a combination of physico-chemical properties including soluble salts, clay content and mineralogy, soil water content, bulk density, organic matter, and soil temperature; consequently, measurements of EC a have been used at field scales to map the spatial variation of several edaphic properties: soil salinity, clay content or depth to clay-rich layers, soil water content, the depth of flood deposited sands, and organic matter. In addition, EC a has been used at field scales to determine a variety of anthropogenic properties: leaching fraction, irrigation and drainage patterns, and compaction patterns due to farm machinery. Since its early agricultural use as a means of measuring soil salinity, the agricultural application of EC a has evolved into a widely accepted means of establishing the spatial variability of several soil physico-chemical properties that influence the EC a measurement. Apparent soil electrical conductivity is a quick, reliable, easy-to-take soil measurement that often, but not always, relates to crop yield. For these reasons, the measurement of EC a is among the most frequently used tools in precision agriculture research for the spatio-temporal characterization of edaphic and anthropogenic properties that influence crop yield. It is the objective of this paper to provide a review of the development and use of EC a measurements for agricultural purposes, particularly from a perspective of precision agriculture applications. Background information is presented to provide the reader with (i) an understanding of the basic theories and principles of the EC a measurement, (ii) an overview of various EC a measurement techniques, (iii) applications of EC a measurements in agriculture, particularly site-specific crop management, (iv) guidelines for conducting an EC a survey, and (v) current trends and future developments in the application of EC a to precision agriculture. Unquestionably, EC a is an invaluable agricultural tool that provides spatial information for soil quality assessment and precision agriculture applications including the delineation of site-specific management units. Technologies such as geo-referenced EC a measurement techniques have brought precision agriculture from a 1980's concept to a promising tool for achieving sustainable agriculture.