Abstract
Practicing agriculture is a multiparametric and for this reason demanding task. It involves the management of many factors and thorough strategic planning in a highly variable and uncertain environment. Crop production is a function of agricultural practices as applied in natural resources, such as soil and plants. When referring to conventional agriculture, variability in these resources is neglected, as any field is treated homogenously. On the other hand, site-specific crop management, which was promoted through the advance of technologies, regarding collecting and analyzing data and applying agricultural decisions at a sub-field level, considers field spatial and temporal variations. Localizing inputs in a field rationalizes agricultural waste management and offers promising perspectives towards a circular economy. In this context, two cotton fields in central Greece were selected for this study. During the growing period, reflectance data were acquired, before planting at the end of April, and 100 days after planting at the end of July, with a commercial unmanned aerial system (UAS). The fields were grid sampled for soil (clay content, pH, calcium carbonate percentage, organic matter, total nitrogen, and electrical conductivity) and plant properties (total nitrogen, potassium, iron, copper, and zinc) determination. All data were manipulated through geographical information systems (GIS) and further participated in principal component analysis (PCA) application. PCA revealed important relations and groupings between soil reflectance and organic matter, carbonates, and clay content in both fields (72 to 87% of the total variance in the initial parameters was explained by the extracted components). However, in plant data, the resulting components accounted for less variability in initial data (62 to 72%). PCA resulting scores were introduced in the Fuzzy c-means clustering algorithm, which categorized sub-areas of the fields into two discrete zones per field. Zoning, in the case of soil properties, was accompanied with the statistically important (p < 0.01) discrimination of the mean values (except for total nitrogen and pH), implicating a promising zonal management scheme. The zone delineation process regarding plant properties yielded areas that did not share statistically significant variations, except for the mean values of iron concentration (p < 0.01). According to the results, spatial variations were revealed across the fields, mostly in soil properties, which can be directly monitored through aerial reflectance data. The applied methodology can be used in extension services or by agronomists for producing fertilizer application maps. Further, when integrated with a broader spatial decision support system, it can be used by policy makers for adapting circular economy strategies in crop production.
Highlights
Introduction distributed under the terms andA physical environment has a dynamic behavior, as it is an open system and involves variable driving forces that interact with each other in a spatial and temporal scale [1]
In Field 1, NIR was significantly and negatively correlated with clay content, organic matter, carbonates, and total soil nitrogen, while it was positively correlated with electrical conductivity
Increases in organic matter, carbonates, and soil acidity in Field 2 seem to increase NIR digital values. This diverse effect between the two fields may be attributed to noise in NIR values coming from existing weed patches and stones in the surface of Field 2
Summary
A physical environment has a dynamic behavior, as it is an open system and involves variable driving forces that interact with each other in a spatial and temporal scale [1]. An anthropogenic environment is another dynamic system, which intervenes to the functions of the physical environment and highly influences its elements. Sustainability 2021, 13, 2855 this co-existence is the field of agriculture, as farmers are called to work in the physical environment and combine agronomic management practices [2]. In crop cultivation, farmers ought to manage soil, water, and crop resources together with applying inputs and practices, in order to yield maximum quality products without burdening the environment. Soil degradation under climate change, such as evolvements in arid or semiarid environments (e.g., in south–eastern Europe), imperatively demand the development of methodologies and planning for reversing current situation [3,4]
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