Abstract
Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
Highlights
In recent years, there has been an intense growth in the world population, which is projected to reach 9.7 billion by 2050 [1]
Precision agriculture or site-specific management can provide food security and sustainable development [3,6,9,10,11]. It is based on innovative system approaches which comprise several technologies, such as global navigation satellite system (GNSS), geographic information system (GIS), proximal sensing (PS) and remote sensing (RS), artificial intelligence (AI), machine learning (ML), automatic guidance, section control, variable rate technology (VRT) and advanced information processing for timely within- and betweenseason crop managements [12,13,14,15]
The north and south-west parts of the field (Zones 5 and 4) show a more productive crop pattern compared with the west and east parts
Summary
There has been an intense growth in the world population, which is projected to reach 9.7 billion by 2050 [1]. The main purpose of PA is to optimize crop management concerning spatial and temporal variabilities, which results in optimized utilization of farm inputs such as fertilizers, pesticides, herbicides and seeds [16] All of this is aimed at increasing farm profitability and achieving the Sustainable Development Goals (SDGs) such as No Poverty, Zero Hunger and Reduced Inequalities. For such a purpose, a wide range of data and information from field inventory, crop growth and yield patterns must be analysed [17]. With this correctly processed information, agricultural inputs such as fertilizers, water or energy can be applied in a spatially variable manner using homogeneous production zones, i.e., management zones (MZ) [16]
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