Assessing spatial variability of soil and drawing location-specific management zones for coastal saline soils in Ramanathapuram District, Tamil Nadu

The production of crops in saline and alkali-degraded areas is difficult due to the heterogeneous and spatial variation of soil fertility.  First, their spatial variability was analyzed and maps of the spatial distribution were created using Geostatistical techniques.  The fuzzy k-mean clustering analysis was then used to define Management zones in the coastal saline soils of Ramanathapuram district in Tamil Nadu.  One hundred and fifty geo-referenced soil samples  (30 cm depth) were taken and analyzed for pH, electrical conductivity (ECe) in the saturated paste extract (USSL method), organic carbon (OC) (Walkley-Black chromic acid wet oxidation method), calcium carbonate (CaCO3) (Rapid titration method) and available phosphorus and extractable micronutrients (Multinutrients extraction method), revealing significant variation in soil characteristics throughout the coastal saline soils of Ramanathapuram district.  The most significant factors, which together accounted for four principal components and 69% of the overall variability, were pH, electrical conductivity (ECe), calcium Carbonate and available zinc.  According to Geostatistical analysis, the Exponential (pH, OC (organic carbon), P, Fe, Mn and Zn) and Stable (ECe) was the best fit semivariogram ordinary kriging model with weak to moderate spatial dependence.  Fuzzy k-mean clustering was also used to identify zone 1, zone 2 and zone 3.  For every soil property, there was a significant difference between MZ1(zone 1), MZ2(zone 2) and MZ3(zone 3).  These results also showed that cluster analysis gave farmers a chance to use location-specific nutrient management strategies by minimizing variability within the zone. The management zones can decrease agricultural inputs and environmental pollutants while increasing crop productivity.

Integrating Management Zones and Canopy Sensing to Improve Nitrogen Recommendation Algorithms

Fertilizer nitrogen use efficiency (NUE) in maize (Zea mays L.) production is historically inefficient, presenting significant environmental and economic challenges. Low NUE can be attributed to poor synchrony between soil N supply and crop demand, applying uniform rates of N fertilizer to spatially variable landscapes, and failure to account for temporal variability in crop response to N. Innovative N management strategies, including crop canopy sensing and management zones (MZ), are tools that have proven useful in increasing NUE. Several researchers have proposed that the integration of these two approaches may result in further improvements in NUE and in profitability by synthesizing both crop- and soil-based information for more robust N management. The objectives of this research were to identify soil and topographic variables that could be used to delineate MZ that appropriately characterize areas with differential crop response to N fertilizer and then to test a sensor-based N application algorithm and evaluate the potential of an integrated MZ- and sensor-based approach compared to uniform N management and to sensor-based N management alone. Management zones delineated with a field-specific approach were able to appropriately characterize the spatial variability in in-season crop response to N in all eight fields and in yield response to N in three of six fields. Sensor-based application resulted in significantly increased NUE compared to uniform N management in six of eight fields, and marginal net return was significantly increased in four of eight fields. Delineated MZ appropriately classified areas of differing NUE in six of eight fields. Results from these studies indicate that integrating field-specific MZ and sensor-based N application has potential to increase NUE and profitability compared to sensor-based or MZ-based N management approaches alone. Additional research is needed to explore how to best incorporate static soil information into a sensor-based algorithm that can be generalized for a variety of soil, climatic, and managerial factors. Advisors: Richard B. Ferguson and Joe D. Luc

An economic evaluation of using Management Zones in cotton production

This thesis examines the use of Precision Agriculture technologies to define Management Zones within a multicrop production system. It further evaluates the economic feasibility of implementing spatially variable insecticide applications against conventional blanket treatments with respect to insect pest management in cotton production. The use of geographical information systems was critical in the development of the different yield maps established to determine the level of consistency of management zones across crops over time. Several important concepts, such as data normalization, yield grid maps, inverse distance weighted and stability, were introduced throughout this research to: set the scale of measures to the same basis, facilitate comparison across crops, manipulate the data, and establish a level of confidence, respectively, concerning the use of management zones in crop production. Furthermore, the basic notion behind this study was that if fields can be divided into high/low yielding management zones, the use of variable rate technology, through an ON/OFF prescription application, offers the potential to reduce costs and increase productivity of the field. The capital recovery method was used to evaluate the per acre cost of investing in a precision farming system for gathering site-specific information and performing SVI applications. Results from this study show that the use of yield-based management zones can reveal annual cost reductions and increased profitability for the producer

Multi-thematic delineation of 'natural zones' of arable fields and their correspondence to spatial yield variation

Properties such as soil apparent electric conductivity (ECa), topography and other site-related data (e.g. canopy reflectance from aerial images) vary across field. The agronomic effects of such variability can sometimes be seen in the spatial variations of crop yield on that field. However, yield maps do not always represent the natural boundaries based on site characteristics. Identification of these boundaries as “management zones” (MZ) can be beneficial in crop management and improving crop input use efficiency. A simple methodology is required to delineate such zones. This research presents an effective methodology to delineate MZ in an irrigated and a non-irrigated (rain-fed) arable maize field in New Zealand. Elevation data for the sites were acquired from Google Earth images and a soil survey. Soil ECa was collected from a soil survey with an electromagnetic device. Yield values (t/ha) were obtained from combine harvesters equipped with yield monitor and Global Positioning System (GPS), over the course of four years for the irrigated site, and two years for the non-irrigated site. The yield data was quality controlled using a filtering system to remove outliers and technically non-plausible data. The data sources were combined in Geographic Information Systems (GIS) and three MZ were delineated for each field through standard clustering methods. The maize yields were aggregated per derived MZ to compare yields between different MZ-classes. The results showed that there was some consistency in yields related to the MZ, derived without yield data. In both the non-irrigated and irrigated fields, the lowest yield consistently occurred in the same class each year, however, the MZ-class with the highest yield varied year to year. The results show that it is possible for the studied type of fields to delineate ‘natural’ clusters or zones of site properties that can be used as MZ-classes as they represent different yield levels. The required inputs are freely available and easily obtained data

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

Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain)

The livestock sector in the Valencian Community (Spain) has experienced an increase in the intensity of farming with an increase in the number and size of pig and poultry facilities. The absence of previous environmental requirements in this region has produced a high concentration of facilities in some areas, and urban sprawl has resulted in many farms located in problematic areas close to villages or towns, residential areas and protected areas. Conflicts surrounding land use and environmental issues have been a problem in the region for many years. The initial step to solve this problem is to produce a territorial planning system to intervene and correct the current development and adapt to new European environmental regulations. The objectives of this study are to group farms with homogeneous characteristics in the Valencian Community and to characterise and search for spatial dependency patterns within the livestock sector. These objectives have the final aim of contributing basic scientific information to subsequent administrative planning decisions for livestock. This study presented methodology based on Geographic Information Systems and statistical methods for dividing livestock farms into zones and for characterising these areas. We obtained nineteen livestock geographical areas with unique characteristics (such as livestock species composition) and verified that these areas did not follow a spatial pattern.Calafat Marzal, MC.; Gallego Salguero, AC.; Quintanilla García, I. (2015). Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain). Computers and Electronics in Agriculture. 114:58-67. doi:10.1016/j.compag.2015.03.005586711