Use of Farmer Knowledge in the Delineation of Potential Management Zones in Precision Agriculture: A Case Study in Maize (Zea mays L.)

Abstract

Abstract:One of the fields of research in precision agriculture (PA) is the delineation of potential management zones (PMZs, also known as site-specific management zones, or simply management zones). To delineate PMZs, cluster analysis is the main used and recommended methodology. For cluster analysis, mainly yield maps, remote sensing multispectral indices, apparent soil electrical conductivity (ECa), and topography data are used. Nevertheless, there is still no accepted protocol or guidelines for establishing PMZs, and different solutions exist. In addition, the farmer's expert knowledge is not usually taken into account in the delineation process. The objective of the present work was to propose a methodology to delineate potential management zones for differential crop management that expresses the productive potential of the soil within a field. The Management Zone Analyst (MZA) software, which implements a fuzzy c-means algorithm, was used to create different alternatives of PMZ that were validated with yield data in a maize (Zea mays L.) field. The farmers' expert knowledge was then taken into account to improve the resulting PMZs that best fitted to the yield spatial variability pattern. This knowledge was considered highly valuable information that could be also very useful for deciding management actions to be taken to reduce within-field variability.

Management Class Delimitation in a Soybean Crop Using Orbital Images

“…For the other cluster analysis, the recommended number of classes was the same according to both indices, with three management classes when using the yield map and two management classes when using the vegetation index maps that had a significant correlation with the crop yield. Martínez-Casasnovas et al (2018) obtained similar results when using the accumulated NDVI to generate management classes using the fuzzy cmeans algorithm. Figure 2 shows the resulting management class maps for the different input variable combinations.…”

“…Once defined, the number of soil samples needed to characterize the variables in the production system is reduced (Valente et al, 2014). The crop yield, topographic data, soil apparent electrical conductivity and remote sensing multispectral index are the most frequently used variables when defining the CMs by cluster analysis (Martínez-Casasnovas et al, 2018).…”

“…Multispectral images are provided by two satellites, Sentinel-2A and Sentinel-2B, which are equipped with identical multispectral instruments (MSI), and it is possible to acquire data in 13 bands at different spatial resolutions (between 10 m and 60 m) using this program. Martínez-Casasnovas et al (2018) used an NDVI (Normalized Difference Vegetation Index) derived from Sentinel-2A images, soil apparent conductivity data, and a digital elevation model for delimiting the management classes in an irrigated maize crop. The authors established a model to complement the automated process of delimiting CMs by aggregating farmer knowledge and thus obtaining better results.…”

An Integrated Approach to Assessing the Soil Quality and Nutritional Status of Large and Long-Term Cultivated Rice Agro-Ecosystems

“…The mean annual precipitation and temperature in the study area are 440 mm and 15.8 o C, respectively, The primary and most important procedure for assessing soil quality status is soil analysis. Zonation through remote sensing techniques (RSTs) and geographical information systems (GISs) have played a key role for a) optimizing the number of sampling sites for the development of representative sampling networks based on the detection of spatial soil differences and crop growth anomalies [26][27][28], b) the identification of the possible origin of such differences/anomalies through in situ observations/samplings, and c) the design of faster and more targeted interventions. On the other hand, it is very important to stress the high cost of such actions in large agricultural areas and the need for additional indirect methods in order to further reduce their cost through even more targeted soil samplings and soil management interventions.…”

Towards Economic Land Evaluation at the Farm Scale Based on Soil Physical-Hydrological Features and Ecosystem Services

“…The use of remote and/or proximal informative layers, which drive the soil survey, is essential and frequently used [13]. Apparent electrical conductivity (ECa), or electrical resistivity (ER), and mapping through electromagnetic induction (EMI) has been commonly used in the last decades for precision agriculture research, especially in understanding yield variability within fields [14].…”