An integrated approach to site-specific management zone delineation

Abstract

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and costeffective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%-9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%-38.1%), and soil nutrient and pH variability (9.4%-14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%-48.9%, 16.1%-17.3% and 13.2%-18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.

Spatial and Temporal Variability of Spring Barley Yield and Quality Quantified by Crop Simulation Model

“…Historical yield maps were created to establish management zones, which were defined as sub−areas within the field. It was thought that these zones had similar yield-limiting factors and, therefore, can be potentially managed in a similar way [10,16]. In this study, the management zones were calculated by using a recently published method by [17,18].…”

“…Site-specific N management offers a possibility to optimize the environmental vs. economic trade−off [9]. However, in many agricultural systems, the adoption of site-specific management has been lower than expected because of technological and socio-economic challenges [10]. The amount of N applied within a field may vary spatially and temporally among years.…”

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

“…Though not pertaining specifically to soil salinity, Miao et al (2018) used a combination of soil and yield information as done by and Scudiero et al (2013) to develop an integrated approach. Miao et al (2018) developed an integrated approach for delineating SSMUs using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS). Their ROSE-YSTTS approach was able to account reasonably effectively for three sources of variability for soil and landscape, nutrient level, and pH.…”

Integrating geospatial tools and a crop simulation model to understand spatial and temporal variability of cereals in Scotland

“…Nowadays, the world is facing the biggest challenge of producing more food on the same or less land, and by reducing its environmental footprint while keeping farming profitable (Cammarano et al, 2016). Precision agriculture (PA) offers a unique opportunity to achieve such trade-off, but its adoption for agricultural management has been slower than expected due to technological and socio-economic challenges (Miao et al, 2018). The amount of any agricultural input applied (e.g.…”