Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties.

Delineation of Management Zones in Hedgerow Almond Orchards Based on Vegetation Indices from UAV Images Validated by LiDAR-Derived Canopy Parameters

One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations.

Groundwater management zones and their groundwater water level thresholds in the Tongliao plain

The West Liao River (WLR) basin located in Inner Mongolia, is an important food production area in China. In recent years, the problem of groundwater over exploitation has become increasingly prominent in the basin due to the expansion of agriculture. This paper developed adaptive management initiatives of the local groundwater resources using Tongliao, located in the east part of the WLR basin, as a case study. Groundwater management zones were divided based on hydrogeology, precipitation, land use, the groundwater over exploitation areas, groundwater depth, and the administrative units (Banners/ Counties/ Districts). The Tongliao basin was divided into 21 management zones. Subsequently, assessment rules for determining groundwater level thresholds in each groundwater management zone were developed based on groundwater observation conditions and the current groundwater depth. Based on the assessment rules, in 2020 the management threshold of groundwater level for each zone were determined. The results provided a scientific basis for the ‘Water Availability Based Local Development Initiative’ in the Tongliao plain.

Implementation of Forestry Best Management Practices and Sediment Delivery in Three Regions of North Carolina and Virginia

Forestry best management practices (BMPs) were created in response to the Clean Water Act of 1972 to protect water quality from nonpoint source pollutants such as sediment. The objectives of this study were to quantify the relationship between BMP implementation and sediment delivery on 58 recently harvested sites across three physiographic regions and five forest operational features. BMP implementation rates, erosion rates, sediment delivery ratios, and sediment masses were calculated at 183 silt fences functioning as sediment traps adjacent to streams in Virginia and North Carolina. Major access system features, including stream crossings, skid trails, and haul roads, typically delivered the greatest sediment mass to streams and had the highest sediment delivery ratios on a per feature basis. When accounting for sediment mass delivered and area in each feature, harvest area accounted for approximately 70% of sediment delivered to streams for all regions. Most features had proportionally higher erosion rates than sediment masses collected at silt fences, indicating that most erosion generated by forest operations is being trapped by either harvest areas or streamside management zones. For most features and regions, as BMP implementation increased, erosion rates and the sediment masses delivered to streams decreased. Study Implications Forestry best management practices (BMPs) are designed to mitigate the amount of sediment entering streams and affecting other aquatic features as a result of forest operations. In this study, a significant inverse relationship between BMP implementation and the amount of sediment delivered to streams was found, indicating that increasing levels of BMP implementation reduces sediment delivery. Most of the erosion caused by forest operations is being trapped before it is delivered to streams. This research highlights the importance of leaving streamside management zones along streams and minimizing the extent of bare soil and area in temporary and permanent roads.

Characterization of the soil compaction based on the mapping of the apparent electrical conductivity

Based on the measurement of soil penetration resistance (PR), it is possible to identify compacted soil layers, where root growth may be harmed, affecting crop development and yield. The objective of this work was to analyze the use of management zones (MZ), delimited on the basis of mapping of the spatial variability of the soil apparent electrical conductivity (ECa), in the differentiation of soil compaction levels. The work was carried out in a 25.8-ha no-tillage area, cultivated under a center pivot. The ECa was measured under two soil moisture conditions (13.7 and 16.45%), using the Terram® equipment. Soil penetration resistance (PR) was measured using the SoloStar PLG5500 penetrograph. Based on the spatial variability ECa mapping, management zones (2, 3, and 4 zones) were delimited. The mean PR values ??of each MZ were compared by the t-test of means. It was possible to differentiate mean values ??of penetration resistance (PR), which vary from 0.9 to 2.10 MPa, from the characterization of management classes generated on the basis of the ECa spatial variability. The highest stratification of PR values ??was obtained as a function of sampling directed at delimited management zones when the soil had lower moisture content (13.7%). The highest mean PR values ??were obtained for the split of the ECa map into at least three classes. It was identified that for the study area there is no need to perform any mechanical decompaction operation.

Mapping Spatial Management Zones of Salt-Affected Soils in Arid Region: A Case Study in the East of the Nile Delta, Egypt

Soil salinization is a global problem that affects a large part of the world, especially arid and semi-arid regions. Hence, diagnosing soil salinity is the first step towards appropriate management. The current work aims to assess and map soil salinity in the eastern Nile Delta using principal component analysis (PCA). In order to develop appropriate solutions for rational management to mitigate the impacts of soil salinization and increase yield production 34 soil profiles were dug that covered the variation in the soils located at the northeast of the Nile delta. The spatial variation of soil parameters was mapped using ordinary kriging interpolation. The results of PCA illustrated that, among the studied soil properties, soil electrical conductivity (ECe), sodium adsorption ratio (SAR), exchangeable sodium percent (ESP), and bulk density (BD), are the critical factors affecting management practices in the Nile Delta. Two spatial management zones (SMZ) were identified; SMZ 1 occupied 45.04% of the study area and SMZ2 occupied 54.96% of the study area. The average of soil pH, ECe, SAR, CEC, ESP and BD were 8.31, 20.32 dSm−1, 47.19, 32.9 cmolckg−1, 32.85% and 1.47 Mgm−3 for the first cluster (SMZ1), respectively. In addition, the second cluster (SMZ2) had average soil pH, ECe, SAR, CEC, ESP and BD of 7.75, 12.30 dSm−1, 26.6, 25.23 cmolckg−1, 26.6% and 1.27 Mgm−3. The results showed p-value < 0.05 which confirms that there is a significant statistical difference between the two zones. Finally, the results obtained could be used as a fundamental basis for improving agricultural management practices in such salt-affected soils.