Soil erosion is a serious environmental threat in the Mediterranean region due to torrential rainfalls, and it contributes to the degradation of agricultural land. Techniques such as rainwater harvesting may improve soil water storage and increase agricultural productivity, which could result in more effective land usage. Reservoir tillage is an effective system of harvesting rainwater, but it has not been scientifically evaluated like other tillage systems. Its suitability for the conditions in Spain has not been determined. To investigate and quantify water storage from reservoir tillage and how it could be adapted to improve infiltration of harvested rainwater, a laboratory-scale rainfall simulator was developed. Rainfall characteristics, including rainfall intensity, spatial uniformity and raindrop size, confirm that natural rainfall conditions are simulated with sufficient accuracy. The simulator was auto-controlled by a solenoid valve and three pressure nozzles were used to spray water corresponding to five rainfall intensities ranging from 36 to 112 mm h-1 for 3 to 101-year return period with uniformity coefficients between 83 and 94%. In order to assess the reservoir tillage method under surface slopes of 0, 5, and 10%, three soil scooping devices with identical volume were used to make depressions in the following forms: a) truncated square pyramid, b) triangular prism, and c) truncated cone. These depressions were compared to a control soil surface with no depression. For the loam soil used in this study, results show that reservoir tillage was able to reduce soil erosion and surface runoff and significantly increase infiltration. There was significant difference between the depressions and the control. Compared to the control, depression (a) reduced surface runoff by about 61% and the sediment yield concentration by about 79%

The efficient management of nitrogen (N) on a site-specific basis is critical for the improvement of crop yield and the reduction of environmental impacts. This review examines the application of three primary technologies—canopy reflectance sensors, chlorophyll meters, and leaf color charts—in the context of site-specific N fertilizer management. It delves into the development and effectiveness of these tools in assessing and managing crop N status. Reflectance sensors, which measure the reflection of light at specific wavelengths, provide valuable data on plant N stress and variability. The advent of innovative sensor technology, exemplified by the GreenSeeker, Crop Circle sensors, and Yara N-Sensor, has facilitated real-time monitoring and precise adjustments in fertilizer N application. Chlorophyll meters, including the SPAD meter and the atLeaf meter, quantify chlorophyll content and thereby estimate leaf N levels. This indirect yet effective method of managing N fertilization is based on the principle that the concentration of chlorophyll in leaves is proportional to the N content. These meters have become an indispensable component of precision agriculture due to their accuracy and ease of use. Leaf color charts, while less sophisticated, offer a cost-effective and straightforward approach to visual N assessment, particularly in developing regions. This review synthesizes research on the implementation of these technologies, emphasizing their benefits, constraints, and practical implications. Additionally, it explores integration strategies for combining these tools to enhance N use efficiency and sustainability in agriculture. The review culminates with recommendations for future research and development to further refine the precision and efficacy of N management practices.

This research study underscores the importance of effectively managing soil nutrients in a site-specific manner to enhance crop productivity while considering the spatial variability of the soil. The objective is to identify subfields with similar soil characteristics, referred to as management zones (MZs), to promote sustainable land utilization. This study was conducted in two central pivot fields located in Southern Idaho, USA, where barley and sugar beets were grown. Soil samples were collected from each field in a grid pattern and analyzed for various chemical properties. These properties included soil pH, organic matter, cation exchange capacity, excess lime, electrical conductivity, total inorganic nitrogen, phosphorus, potassium, calcium, magnesium, zinc, iron, manganese, copper, and boron. Descriptive statistics and normality assessments were performed, and the coefficient of variation was calculated to assess the heterogeneity of soil properties, revealing significant variability. To determine the spatial variability of soil properties, ordinary kriging was used revealing diverse spatial patterns for each location and soil variable examined with moderate to strong spatial dependence. To develop the MZs, a combination of principal component analysis and fuzzy k-means clustering was utilized, and specific parameters that represented the overall variability of soil properties in each field were identified. Based on the identified parameters, two clusters were created in each field. The first management zone (MZ1) exhibited lower values of soil pH, excess lime content, and electrical conductivity compared to the MZ2. Consequently, higher crop productivity was observed in MZ1 in both fields. The biomass yields of barley and sugar beets in MZ1 surpassed those in MZ2. This study highlights the effectiveness of the methodology employed to delineate MZs, which can be instrumental in precise soil nutrient management and maximizing crop productivity.