The major global challenge for the coming decades will be increasing crop production with less water consumption. Precision agriculture (PA) and variable deficit irrigation (VDI) are management strategies that help farmers to improve crop production, fertilizer’s efficiency, and water use efficiency (WUE). The effects of irrigation (IR1 = variable deficit irrigation; IR2 = farmers’ irrigation common practices) under three fertilization (Ft1, Ft2, Ft3) treatments were studied on a cotton yield, on various indicators for more efficient water and fertilizer use, and on plant growth characteristics by applying a number of new agrotechnologies (such as TDR sensors; soil moisture (SM); PA; remote-sensing NDVI (Sentinel-2 satellite sensors); soil hydraulic analyses; geostatistical models; and SM root-zone modelling 2D GIS mapping). The reference evapotranspiration was computed based on the F.A.O. Penman–Monteith method. The crop (ETc) and actual (ETa) evapotranspiration were computed using crop coefficients obtained from the remote-sensing NDVI vegetation index (R² = 0.9327). A daily soil–water–crop–atmosphere (SWCA) balance model and a depletion model were developed using sensor data (climatic parameters’ sensors, as well as soil and satellite sensors) measurements. The two-way ANOVA statistical analysis results revealed that irrigation (IR1 = best) and fertilization treatments (Ft2 = best) significantly affected the cotton yield, the plant height, the plant stem, the boll weight, the above-ground dry matter, nitrogen and fertilizer efficiency, and WUE. VDI, if applied wisely during critical growth stages, could result in a substantial improvement in the yield (up to +28.664%) and water savings (up to 24.941%), thus raising water productivity (+35.715% up to 42.659%), WUE (from farmers’ 0.421–0.496 kg·m⁻³ up to a VDI of 0.601–0.685 kg·m⁻³), nitrogen efficiency (+16.888% up to +22.859%), and N-P-K fertilizer productivity (from farmers’ 16.754–23.769 up to a VDI of 20.583–27.957).

Precision agriculture (PA), management zone (MZ) strategies at the field level, soil analyses, deficit irrigation (DI), and fertilizer Variable Rate Application (VRA) are management strategies that help farmers improve crop production, fertilizer use efficiency, and irrigation water use efficiency (IWUE). In order to further investigate these management strategies, the effects of four soil MZ treatments, which were delineated using PA with fuzzy k-means clustering, two irrigation levels [IR1:FI = full drip irrigation (>90% of ${\theta }_{fc}$), IR2:VDI = variable deficit drip irrigation (60–75% of ${\theta }_{fc}$)], and four VRA fertilizations were studied on coriander yield and essential oil content in a two-year research project in Greece. A daily soil-water-crop-atmosphere (SWCA) balance model and a daily depletion model were developed using sensor measurements (climatic parameter sensors as well as soil moisture sensors). Unbalanced one-way ANOVA (p = 0.05) statistical analysis results revealed that correct delineation of MZs by PA with fuzzy k-means clustering, if applied under deficit irrigation and VRA fertilization, leads to increased essential oil content of coriander with statistically significant differences (SSD) and lower fruit yields; however, without SSD differences among management zones, when appropriate VRA fertilization is applied to leverage soil nutrient levels through the different fuzzy clustered MZs for farming sustainability. Moreover, VDI compared to full irrigation in different MZs yields 22.85% to 29.44% in water savings, thus raising IWUE (up to 64.112 kg m⁻³), nitrogen efficiency (up to 5.623), and N-P-K fertilizer productivity (up to 5.329).

The objective of our investigation was to study the various effects of correct and incorrect application of fuzziness exponent, initial parameterization and fuzzy classification algorithms modeling on homogeneous management zones (MZs) delineation of a Coriandrum sativum L. field by using precision agriculture, soil chemical, granular and hydraulic analyses, fuzzy k-means zoning algorithms with statistical measures like the introduced Percentage of Management Zones Spatial Agreement (PoMZSA) (%), factor and principal components analysis (PCA) and geostatistical nutrients GIS mapping. Results of the exploratory fuzzy analysis showed how different fuzziness exponents applied to different soil parameter groups can reveal better insights for determining whether a fuzzy classification is a correct or incorrect application for delineating fuzzy MZs. In all cases, the best results were achieved by using the optimal fuzziness exponent with the full number of parameters of each soil chemical, granular and hydraulic parameter group or the maximum extracted PCAs. In each case study where the factor analysis and PCA showed optimal MZs > 2, the results of the fuzzy PoMZSA clustering metric revealed low, medium and medium to high spatial agreement, which presented a statistically significant difference between the soil parameter datasets when an arbitrary or commonly used fuzziness exponent was used (e.g., φ = 1.30 or φ = 1.50). Soil sampling and laboratory analysis are tools of major significance for performing exploratory fuzzy analysis, and in addition, the FkM Xie and Benny’s index and the introduced fuzzy PoMZSA clustering metric are valuable tools for correctly delineating management zones.

The aim of our study is the modeling at the field level of the soil erodibility (K factor) by water (rainfall and irrigation) on traditional tillage (CoTl) and no-tillage (NoTl) plots cultivated with Helianthus annuus utilizing plot observations, soil sampling laboratory analyses, GIS, precision agriculture (PA), and Kriging geostatistical modeling. A split-plot layout consisting of four handlings × three replicates of trial blocks (with a southeast facing 7.5% slope) was used. Grid template surface soil core (0.0–5.0 cm) samples were taken to characterize the textures (sandy, silty, clayey, very fine sandy, and gravelly), organic matter concentrations, and the soil’s microstructure and water permeability categories. One GPS satellite tracker system was utilized to define the sampled positions, and 40 soil cores were air-dried and sieved with a 2 mm sieve to identify the soil’s mechanical microtexture using the Bouyoucos methodology. The organic matter was extracted by chemical oxidation with 1 mol L⁻¹ K₂Cr₂O₇ and titration of the remaining reagent with 0.5 mol L⁻¹ FeSO₄. The soil microstructure and permeability categories were defined following the USDA classification system. The soil erodibility by water modeling of K (Mg·ha·h·ha⁻¹·MJ⁻¹·mm⁻¹) was derived according to the Wischmeier nomographic method by incorporating it into a developed GIS geospatial model using Kriging geostatistics. The statistical results of the ANOVA test (p = 0.05) among the soil erodibility datasets showed significant differences between the two tillage systems, as well as between the four management treatments. Moreover, it was found that the no-tillage (NoTl) plots and the treatment of no tillage plus vegetative coverage were the best tillage and agricultural practices for hillslope farm fields and can be considered environmentally friendly farming methods to curb soil erodibility by water, reduce runoff hazard, and maintain the soil’s environment and its beneficial nutrients.