The division of an arable field into zones of different productivity requires a reliable, discriminatory tool. This hypothesis was validated by analyzing the spatial variability of yield and N indicators in the crop rotation of winter oilseed rape (WOSR)/winter triticale (WTR) during 2016/2017 and 2017/2018 in a field of 30 ha (Przebędowo, Poland). The direct, measurable variables were: yield, N accumulated in—seeds/grain and crop residues, mineral N in spring, and harvest. The basic N indicators were total N uptake (TN), N-partial factor productivity, and N balance (N_b). The attainable yields of WOSR and WTR were 4.93 and 6.51 t ha⁻¹, and a yield gap of −2.04 and −2.10 t ha⁻¹. The management of 50 kg of the non-used N by crops, i.e. nitrogen gap (NG) could cover 36% and 65% of the yield gap (YG), respectively. The N_b, based on N input (N_in = N_min + N_f) and TN, was the key field indicator, defining both yield and NG. Geostatic parameters, i.e., the nugget to sill ratio, spatial dependence range, and mean correlation distance, were very stable (≤0.2–0.17; 94–100 m; 28 m for WOSR and WTR). The spatial stability of N_b, irrespective of the crop and growing conditions, corroborates its suitability for discriminating high and low-productivity field zones.

The albedo of bare soil depends on its organic matter, iron oxide, carbonate contents, and reflectance geometry, features considered stable over time, and also depends on salinity, moisture and roughness, which change dynamically due to agricultural practices. This paper deals with the quantitative estimation of the amount of shortwave radiation that could be reflected by air-dried bare soils in clear-sky conditions within arable lands in Israel throughout the year, assuming that they were shaped by a plough, a disk harrow, or a smoothing harrow. An area of bare soils was extracted from Landsat 8 images, within the contours of arable lands. The radiation reflected from the bare soils was calculated by equations predicting variations in their half-diurnal albedo as the solar zenith angle function. Accordingly, laboratory reflectance data of Israeli soil samples were used. The results clearly showed annual variation in the amount of short-wave radiation reflected from all bare soils within arable lands. The minimum radiation occurred in the winter, between the 1st and 70th day of the year (DOY), and the maximum was identified in the summer between 200th and 250th DOY. This could reach about 3–5 PJ/day and 16–23 PJ/day, respectively.

Field zones at risk of low nitrogen use efficiency (NUE) can be identified by analyzing in-field spatial variability. This hypothesis was validated by analyzing soil mineral nitrogen (N_min) and several plant and soil N management indices. The research was conducted in Karmin (central Poland) during two growing seasons, with winter oilseed rape (2018/2019) and winter wheat (2019/2020). The study showed that the crop yield was positively related to N_min. However, this N trait did not explain all the observed differences in the spatial variation of crop yield and plant N accumulation. In addition, the soil N management indices were more spatially variable during the growing season than the plant N management indices. Particularly high variability was found for the indices characterizing the N surplus in the soil-plant system. The calculated N surplus (N_b = N fertilizer input − N seed output) ranged from −62.8 to 80.0 kg N ha⁻¹ (coefficient of variation, CV = 181.2%) in the rape field and from −123.5 to 8.2 kg N ha⁻¹ (CV = 60.2%) in the wheat field. The spatial distribution maps also confirm the high variability of the parameters characterizing the post-harvest N surplus, as well as the total N input (soil + fertilizer) to the field with rape. The results obtained indicate that a field N balance carried out in different field zones allows a more accurate identification of potential N losses from the soil-plant system.

The size of the microbial biomass and the activity of soil enzymes are among the most sensitive indicators of agricultural land quality. The aim of this study was to determine the spatial and temporal variability of microbial biomass, the activity of dehydrogenase (DHA) enzyme and the concentration of micro- (Na, Mg and Ca) and macroelements (Cu, Zn, Mn and Fe) in the soil, collected from 37 measurement sites (depth 0–30 cm) in a 40-hectare field during two growing seasons (wheat and oilseed rape). The percentage of nitrogen (%N) in the wheat grain and rapeseeds was also determined. Mapping the spatial distribution of the microbial biomass, the level of DHA activity and the concentration of the selected elements was used to assess the soil productivity. All tested soil parameters exhibited temporal and spatial variability. The creation of raster maps showing the distribution of the tested parameters allowed the observation of a higher nitrogen content in wheat grains in the south-western part of the field, with high values of DHA activity, bacterial biomass and soil pH. In the case of oilseed rape, plants grown in the northern part of the field were characterized by a higher nitrogen content in the grain, where the soil was characterized by a higher content of fungal biomass. On the basis of the obtained research results, a positive, statistically significant correlation was also shown between the biomass of bacteria and the level of DHA in the soil under the cultivation of both wheat and rape. The cultivation of both crops had a significant impact on the size of the microbial biomass pool and on the DHA activity level but did not affect the concentration of the nutrients in the soil. High concentrations of the analyzed elements at the measuring points correlated with a greater %N content in the grain/seeds of the crops harvested at those locations in the field. The results conclude that the mapping of the physicochemical parameters, microbial biomass and activity on the field permits the development of an effective strategy for maintaining sustainable soil productivity through the appropriate management of agricultural practices and the better approximation of mineral fertilization.

Understanding the spatial–temporal variability of soil enzymatic activity and its relationship with nitrogen (N) resources in the soil and crop yield is crucial in rational management practices of mineral fertilization. The scarcity of comprehensive studies on geostatic analyses of agricultural soils and plant yields, which would take into account both temporal and spatial variability, was the reason for undertaking this research. The aim of this study was to determine the spatial and temporal variability of the activity of soil enzymes, such as acid (PAC) and alkaline (PAL) phosphatases, urease (URE) and protease (PROT), the content of N-NH₄ (ammonium ions), N-NO₃ (nitrate ions), phosphorus (P), pH, moisture, as well as crop yield on a conventionally managed farmland of 40 ha. During the two-year experiment, soil samples were collected from 37 measurement points. Wheat was the first tested crop, followed by oilseed rape. It was shown that all the tested soil parameters showed temporal and spatial variability, and a significant number of them were significantly higher in July. The creation of raster maps showing the distribution of the tested parameters allowed for the observation of the considerable activity of PAC, PAL, URE, and PROT, as well as a high application of N-NO₃ in the southern part of the field during the growth of the plants. The statistical analysis revealed a negative interaction between the N-NH₄ and N-NO₃ and the urease in the soil under the cultivation of plants. The pH and the percentage of moisture in the soil also had higher values in the south of the field. This pointed to the existence of separate production zones in the south-central part of the field, characterized by a higher yield of wheat and rape. On the basis of the conducted research, it was unequivocally stated that the values of enzymatic and chemical parameters of the soil were reflected in the size of the yield obtained, which allows conclusions to be drawn with respect to the rational management of N in the production process, laying the foundations for precision agriculture.

Accurate and timely assessment of crop disease severity is crucial for effective management strategies and ensuring sustainable agricultural production. Traditional visual disease scoring methods are subjective and labor-intensive, highlighting the need for automated, objective alternatives. This study evaluates the effectiveness of a model for field-based identification and quantification of stripe rust severity in wheat using red, green, blue RGB imaging. Based on crop reflectance hyperspectra (CRHS) acquired using a FieldSpec ASD spectroradiometer, two complementary approaches were developed. In the first approach, we estimate single leaf disease severity (LDS) under laboratory conditions, while in the second approach, we assess crop disease severity (CDS) from field-based RGB images. The high accuracy of both methods enabled the development of a predictive model for estimating LDS from CDS, offering a scalable solution for precision disease monitoring in wheat cultivation. The experiment was conducted on four winter wheat plots subjected to varying fungicide treatments to induce different levels of stripe rust severity for model calibration, with treatment regimes ranging from no application to three applications during the growing season. RGB images were acquired in both laboratory conditions (individual leaves) and field conditions (nadir and oblique perspectives), complemented by hyperspectral measurements in the 350–2500 nm range. To achieve automated and objective assessment of disease severity, we developed custom image-processing scripts and applied Random Forest classification and regression models. The models demonstrated high predictive performance, with the combined use of nadir and oblique RGB imagery achieving the highest classification accuracy (97.87%), sensitivity (100%), and specificity (95.83%). Oblique images were more sensitive to early-stage infection, while nadir images offered greater specificity. Spectral feature selection revealed that wavelengths in the visible (e.g., 508–563 nm and 621–703 nm) and red-edge/SWIR regions (around 1556–1767 nm) were particularly informative for disease detection. In classification models, shorter wavelengths from the visible range proved to be more useful, while in regression models, longer wavelengths were more effective. The integration of RGB-based image analysis with the Random Forest algorithm provides a robust, scalable, and cost-effective solution for monitoring stripe rust severity under field conditions. This approach holds significant potential for enhancing precision agriculture strategies by enabling early intervention and optimized fungicide application.