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The study aimed to evaluate the spatial distribution of soil pH, electrical conductivity (EC), and particle size distribution within a seven-hectare field in Los Baños Laguna, Philippines using the ordinary kriging method and to utilize the interpolated maps to delineate management zones. Fifty soil samples were collected from the surface layer at a depth of 0–20 cm using a random sampling technique. On the basis of the obtained results, it was found that the area has an acidic pH, medium-textured soil with low soluble salt content. Geostatistical analysis revealed that soil EC and clay content exhibited strong spatial dependence, while soil pH and silt were observed to have a moderate spatial dependence. In contrast, sand exhibited weak spatial dependence. The spherical model was identified as the optimal fit for soil pH, clay content, silt content, and sand content, while the exponential model was deemed most suitable for EC. Three distinct management zones (MZs) were delineated based on the spatial variability of the selected properties. MZ1, the largest zone covering 82.10% of the area, is characterized by a weakly acidic, clay loam soil while MZ2, comprising 15.11% of the area, has a weakly acidic loam soil. MZ3, the smallest zone occupying 2.79% of the area, has a highly acidic loam soil and may require frequent as well as intensive lime applications. These findings highlight the varied spatial dependency and distribution of soil characteristics even in a relatively small area and the usefulness of the interpolated maps as a valuable tool to identify specific management zones. [ABSTRACT FROM AUTHOR]

The study aimed to evaluate the spatial distribution of soil pH, electrical conductivity (EC), and particle size distribution within a seven-hectare field in Los Baños Laguna, Philippines using the ordinary kriging method and to utilize the interpolated maps to delineate management zones. Fifty soil samples were collected from the surface layer at a depth of 0-20 cm using a random sampling technique. Based on the results, the area has an acidic, medium-textured soil with low soluble salt content. Geostatistical analysis revealed that soil EC and clay content exhibited strong spatial dependence, while soil pH and silt were observed to have a moderate spatial dependence. In contrast, sand exhibited weak spatial dependence. The spherical model was identified as the optimal fit for soil pH, clay content, silt content, and sand content, while the exponential model was deemed most suitable for EC. Three distinct management zones (MZs) were delineated based on the spatial variability of the selected properties. MZ1, the largest zone covering 82.10% of the area, is characterized by a weakly acidic, clay loam soil while MZ2, comprising 15.11% of the area, has a weakly acidic loam soil. MZ3, the smallest zone occupying 2.79% of the area, has a highly acidic loam soil and may require frequent and intensive lime applications. These findings highlight the varied spatial dependency and distribution of soil characteristics even in a relatively small area and the usefulness of the interpolated maps as a valuable tool to identify specific management zones.

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 (Nmin) 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 Nmin. 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 (Nb = 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. [ABSTRACT FROM AUTHOR]

Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient. [ABSTRACT FROM AUTHOR]

Improving agricultural nitrogen management is one of the key objectives of the recent Green Deal in Europe. Current technological developments in agriculture offer new opportunities to improve nitrogen fertilization practices. The aim of this study was to adapt to Sentinel-2 data a proven delineation method initially developed for yield maps, in order to facilitate precise nitrogen management by farmers. The study was conducted in two steps. Firstly, an analysis at annual level was conducted to assess the relationship between vegetation indices and yield at the subfield scale, for different sensing period. The second step consisted in performing a pluri- annual analysis through the delineation of management zones and compare the results achieved from yield maps and from NDVI maps. Among different vegetation indices, NDVI proved to be an interesting candidate for subfield detection of yield variation, specifically when the index was sensed was sensed around the second half of May. In this area, this period usually corresponds to phenological development between the flag leaf stage and heading stage, just prior the initiation of winter wheat flowering. Using NDVI maps within Blackmore's delineation approach instead of yield maps. Allowed to reach an accuracy of 69% on zone classification. However, as yields and NDVI distribution do not respond to similar statistical distributions, we considered that the delineation threshold used to differentiate high from low yielding zones had to be adapted. The adaptation of the "performance threshold" in favor of the median NDVI, made it possible to achieve a higher accuracy (71%) of the delineation. But above all, the improvement lies also in a more robust satellite-based delineation. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two approaches for mapping soil properties in precision agriculture: management zone (MZs) and contour maps. This is the second paper in a two-part series that focuses on contour maps. Detailed and accurate contour maps of soil properties for precision agriculture are often costly to produce because of the large sampling effort required. Such maps or those of sensed ancillary data are often simplified to represent MZs. This research investigated the accuracy of detailed maps of soil properties produced inexpensively from sensed data by transforming them to z-scores. The z-scores of ancillary values are then transformed to values of soil variables using the mean and standard deviation of a small soil data set. The errors from this mapping approach are examined with historic soil data from three field sites with different scales of spatial variation in the United Kingdom. Errors from the conversion of z-scores of sensed data to soil variable ranges are compared with those from MZ averages (Paper I in this series). For soil properties with a moderate relation to ancillary data, the errors related to the z-score conversion were small irrespective of sample size. The root mean squared errors associated with the MZ mean rather than values from the digital map were generally smaller except when sample size was very small. The results suggest that when the scale of variation is small and more samples are required to define MZs, calibrating z-scores of sensed ancillary data may provide better MZ averages than sampling on a grid; it also provides a detailed map of spatial variation within the field. The z-score conversion approach is less sensitive to sample size and captures small features of the variation compared to the standard 100 m grid sampling to determine MZ averages. [ABSTRACT FROM AUTHOR]

Sensed and soil sample data are used in two main approaches for mapping soil properties in precision agriculture: management zones (MZs) and contour maps. This is the first of two papers that explores maps of MZs. Management zones based on variation in sensed data that are related to the more permanent soil properties assume that the zones are multi-purpose. Soil properties are then often sampled on a grid to provide the average values of each property per zone. This paper examines the plausibility of this approach by examining how the number of samples taken on a grid and the application of kriging affect mean soil property values for MZs. The suitability of MZs based on ancillary data for managing several agronomically important properties simultaneously is also considered. These concepts are examined with historic soil data from four field sites in southern UK with different scales of spatial variation. Results showed that when the grid sampling interval is large, there is less difference in the means of properties between MZs, but kriging the soil data increased the differences between zones when the sampling interval was large and sample small. Sensed data are used increasingly to aid the identification of MZs, but these could not be considered multi-purpose at all sites. The MZs produced were most useful for phosphorus (P), pH and volumetric water content (VWC) at the Wallingford site and useful for most properties at the Clays and Y215 sites. For the latter site this was true only when the most dense data were used to calculate MZ averages. The results show that sampling interval for MZ averages should relate to the scale of variation or the size of the MZs at a site. The sampling density could be based on the variogram range of ancillary data. This research suggests that there should be 6–8 samples per zone to obtain accurate averages of soil properties. Nutrient data for more than one year were examined at two sites and showed that patterns remained consistent in the short term unless variable-rate management was used, but also the range of values changed in the short term. [ABSTRACT FROM AUTHOR]

Background and Objectives Available water holding capacity (AWHC) is a key property for quantifying the amount of water available to plants. This property determines the amount of water required for the crop and the irrigation time interval and is related to the inherent productivity of the soil. Determining the homogeneous zones of available water-holding capacity is a possible way to evaluate the contribution of the soil in the optimal input of irrigation water in paddy fields. The rice fields of Guilan province, located in the north of Iran, with an area of about 238,000 hectares, are one of the most important rice cultivation areas because more than 30% of the country's rice is produced in this area. Irrigation in this area is done in the form of uniform flooding for easier transfer of rice seedlings, better water retention and weed control. In recent years, due to drought, population growth and increasing urban and industrial demand for water and inefficient management of flood irrigation, an attempt has been made to manage irrigation in the form of intermittent irrigation. Therefore, determining the homogeneous zones of water storage capacity management is a possible way to evaluate the contribution of soil in the optimal input of irrigation water in paddy fields. Because with its help, it is possible to manage the appropriate time of drying and wetting of paddy fields in intermittent irrigation. Water resource management is a priority to reduce productivity instability and negative socio-economic effects. For this purpose, homogeneous water storage capacity management zones were determined in Kouchesfahan region to investigate the physical-hydraulic conditions of paddy soils, dividing the entire region into zones with the same potential for water storage capacity and investigating its relationship with rice yield. Methodology A total of 131 undisturbed and disturbed soil samples with uniform geographic distribution were prepared from the rice root growth area and some chemical, fertility and physical-hydraulic properties were measured. Rice yield was also measured in most of the soil sampling points. Then, among 14 characteristics, seven characteristics (clay, organic carbon, linear expansion coefficient, saturated hydraulic conductivity, average particle diameter, accessible water and integral energy) that can affect the water holding capacity in the soil were selected. The number of homogeneous zones of water storage capacity was determined using two statistics, fuzzy efficiency index and modified classification entropy index. Then, by using the concepts related to algebraic maps, the integration of information layers was done in the GIS environment and the relevant maps were prepared. Findings The results of the fuzzy efficiency index and the entropy index showed that the investigated area can be divided into four water storage capacity areas. The lowest and highest value of the average yield was seen in the first and fourth zones, respectively. A significant difference was seen between available water, organic carbon, Coefficient of Linear Extensibility (COLE), integral energy and Saturated Hydraulic Conductivity (Ks) in four management zones, but no significant difference was seen in the amount of clay and the mean diameter of the pores. In the first zone, organic carbon, Coefficient of linear extensibility (COLE), Median of Soil Pore Size Distribution (dmedian) and Ks showed the lowest values, but in the fourth zone, all these properties have the opposite behavior. The yield in the first zone showed an increasing trend from 3971 kg ha-1 to 4312 kg ha-1 in the fourth zone. Conclusion These results showed that the available water-holding capacity increases from zone one to zone four. Therefore, one and two zones will be particularly sensitive during drought and lack of moisture during the growing season, and the management of these zones needs special attention. In these zones, the irrigation cycle (irrigation time interval) should be shorter than the other two areas, in order to avoid the occurrence of drought stress. The soils of the studied area were uneven in terms of water retention. These results showed that the uniform (fixed) management of water consumption, in addition to increasing costs, can also lead to the waste of a large amount of water. In this situation, location-specific irrigation management can be more efficient in sustainable economic production. The comparison of the estimation map of homogeneous zones shows the water storage capacity and yield, in some zones, although the soil conditions are suitable in terms of moisture conditions, the yield is not in optimal conditions. It seems that until the state of fertility is not favorable, physical conditions cannot show their effects in performance well. In other words, until the lack of fertility is not resolved, restrictions or suitable physical conditions will not have clear effects on performance. [ABSTRACT FROM AUTHOR]

Site-specific nitrogen management has been proposed as a tool to increase crop yield while decreasing nutrient losses to the environment. Many reports can be found on sensing technologies to quantify the variability within a field and the definition of management zones based on the observed variability. However, fewer studies have been dedicated to the selection of the most suitable N fertilizer management scenario: should more or less nutrients be applied in the zones with a lower crop productivity potential? To address this knowledge gap, nine Flemish maize fields were selected as potential candidates for precision fertilization based on the soil maps and historical vegetation index patterns. Within each field, two management zones were identified based on historical vegetation index patterns and electrical conductivity maps, and different fertilization strategies were tested in each zone. The field trial results in terms of yield and soil residual nitrate showed that site-specific N management outperforms the conventional practice only in the fields with temporally stable management zones. In the fields having differences in the physical soil properties (e.g. presence of stones or clay particles), affecting water availability, lower fertilization in zones with a poor soil productivity potential could be recommended. In the fields where the performance of the management zones changes from year to year mainly due to annual variation in precipitation, a risk of incorrect implementation of the precision fertilization concept was identified. Historical NDVI time series serve a good basis to delineate the temporally stable management zones. [ABSTRACT FROM AUTHOR]

This study aimed to simulate dynamic irrigation management zones (MZs) in two maize fields for a variable rate hose reel fertigation machine (VRFM) with a four-section boom control. Soil moisture content was measured from nine and four soil moisture sensors in Field 1 (8.2 ha) and Field 2 (2.5 ha), respectively, on different dates during the 2022 crop season. Three and five MZs scenarios were simulated per irrigation and the theoretical maps were processed for implementation. The application maps fitted to the VRFM showed significant spatiotemporal variations in irrigation requirements. For instance, in Field 1, 3-MZ modelling showed that the areas requiring high (H), medium (M), and low (L)-level irrigation on 21 July were 1.60, 4.84, and 1.85 ha, respectively, even though the farmer applied uniform rate over the whole field. H-level sub-areas ranged between 1.22 ha (25 July) and 3.25 ha (7 July), showing a coefficient of variation (CV) of 43.32% for the three MZs, whereas H-level sub-areas for the five MZs varied from 0.41 ha (2 July) to 1.49 ha (7 July) with a CV value of 48.84%. High levels of within-field variability can be addressed using precise and dynamic irrigation MZs fitted to the irrigation technology used. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization. [ABSTRACT FROM AUTHOR]

In precision agriculture, sub‐areas of fields with similar features are called management zones and the inputs applied similarly in these zones. Such notion of management zones is applicable to high‐output soybean fields where thrips are an emerging problem leading to losses up to 15%. Management zones are an agricultural management strategy that can be used for decision‐making for controlling thrips on soybean fields. Thus, a 2‐year study was carried out on commercial soybean fields aimed at developing a decision‐making system for controlling soybean thrips using management zones. Three control programmes were established and assessed: Cl‐conventional control with insecticide use in the entire field; IPM‐CS‐conventional sampling plan + spray upon reaching the economic injury level (EIL; 3.43 thrips/sample); and IPM‐MZ‐spraying only specific areas of the field where thrips density reached the EIL. The decision to control reached using the IPM‐CS and IPM‐MZ programmes was 3× lower than those of the CI, which incurred in 75% mode decision mistakes. Furthermore, the decision to spray insecticides based on analysis of data for Cl was also incorrect. In 8.5% (3.6% of treated and 4.9% of non‐treated) of the field, the decisions of the IPM‐CS programme were incorrect. The total cost of the Cl programme (US$ 11.4/ha) was higher than that of the IPM‐CS and IPM‐MZ programmes (US$ 3.2 ha−1). Therefore, owing to its technical, economic, and environmental advantages, the establishment of management zones seems worthy of incorporation in integrated thrips management decision‐making systems for soybean fields. [ABSTRACT FROM AUTHOR]

The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study’s findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide.

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. [ABSTRACT FROM AUTHOR]

We study studied the alpine meadow soil in Gannan Prefecture. Geostatistical analysis showed that Circular [soil total nitrogen (STN), soil total potassium (STK), soil available phosphorus (SAP)] model, index [bulk density (BD), soil water content (SWC), soil organic carbon (SOC), soil available nitrogen (SAN)] model and Gaussian [soil total phosphorus (STP), soil available potassium (SAK)] model is universal Kriging Method semi-variation function optimization fitting model. In the model, the spatial correlation is weak to medium, and SOC, STN, STP, STK, SAN, SAP, SAK, and SWC have specific spatial trend effects. Then principal component analysis was used to obtain three key factors (SOC, BD, and SWC) affecting soil quality, which accounted for 81.91% of the total variation of soil quality. These factors were taken as the minimum data set (MDS) and soil quality index (SQI) was calculated. According to SQI, the whole region was divided into three soil management zones (SMZs). Recommendations are made for different management areas to ensure resource conservation and reduce environmental footprint. [ABSTRACT FROM AUTHOR]

The precision agriculture scientific field employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impacts. Therefore, obtaining a high number of soil samples to make precision agriculture feasible is challenging. This data bottleneck has been overcome by identifying sub-regions based on data obtained through proximal soil sensing equipment. These data can be combined with freely available remote sensing data to create more accurate maps of soil properties. Furthermore, these maps can be optimally aggregated and interpreted for soil heterogeneity through management zones. Thus, this work aimed to create and combine soil management zones from proximal soil sensing and remote sensing data. To this end, data on electrical conductivity and magnetic susceptibility, both apparent, were measured using the EM38-MK2 proximal soil sensor and the contents of the thorium and uranium elements, both equivalent, via the Medusa MS1200 proximal soil sensor for a 72-ha grain-producing area in São Paulo, Brazil. The proximal soil sensing attributes were mapped using ordinary kriging (OK). Maps were also made using kriging with external drift (KED), and the proximal soil sensor attributes data, combined with remote sensing data, such as Landsat-8, Aster, and Sentinel-2 images, in addition to 10 terrain covariables derived from the digital elevation model Alos Palsar. As a result, three management zone maps were produced via the k-means clustering algorithm: using data from proximal sensors (OK), proximal sensors combined with remote sensors (KED), and remote sensors. Seventy-two samples (0–10 cm in depth) were collected and analyzed in a laboratory (1 sample per hectare) for concentrations of clay, calcium, organic carbon, and magnesium to assess the capacity of the management zone maps created using analysis of variance. All zones created using the three data groups could distinguish the different treatment areas. The three data sources used to map management zones produced similar map zones, but the zone map using a combination of proximal and remote data did not show an improvement in defining the management zones, and using only remote sensing data lowered the significance levels of differentiating each zone compared to the OK and KED maps. In summary, this study not only underscores the global applicability of proximal and remote sensing techniques in precision agriculture but also sheds light on the nuances of their integration. The study's findings affirm the efficacy of these advanced technologies in addressing the challenges posed by soil heterogeneity, paving the way for more nuanced and site-specific agricultural practices worldwide. [ABSTRACT FROM AUTHOR]

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 (Nmin) 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 Nmin. 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 (Nb = 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.

Turfgrass irrigation consumes a large amount of the scarce freshwater in arid/semi-arid regions. Approximately 50% of this irrigation water is wasted. It has been suggested that determining patterns of spatial variability in soil moisture to modify applications with valve-in-head sprinkler technology can greatly reduce waste. Variable rate irrigation (VRI) studies in traditional agricultural settings have shown that VRI zones do not stay static temporally and need to be frequently redetermined. Electrical conductivity (ECa) data from Geonics EM38 surveys and data from Red, Green, Blue (RGB) and Thermal Infra-Red (Th.IR) drone surveys are less time-consuming and therefore expensive to collect than a dense field survey of soil moisture and grass health to produce accurate geostatistical maps. Drone flights and ECa surveys are compared here for their ability to accurately estimate spatial patterns of soil volumetric water content (VWC) using simple linear regression and z-score transformations for prediction—non-geostatistical approaches that require less data. Overall, ECa readings collected in the horizontal mode were the most consistent at capturing spatial patterns in soil moisture. Predictions from regression produced lower root mean squared errors (RMSEs) for the larger datasets. However, z-score transformation produced lower RMSEs when the sample number was very small and preserved the scale of values better than the regression approach. The results suggested that predictions from ECa and drone data were useful for capturing key features in soil moisture patterns for 2–3 weeks, suggesting that a periodic reassessment of zones is needed. Using ECa and drone data in an urban environment is more labor-intensive than in an agricultural field, so it is likely that automating periodic re-surveying of ECa data for zone definition would only be cost-effective for golf courses or high-income sports fields. Elsewhere, using static zones with variable rates applied to each zone may be more efficient.

Integrating ecosystem services (ESs) into the land spatial planning can provide innovative insights for coordinating the spatial conflict between ecological protection and economic development so as to promoting regional sustainable development. However, current studies mostly focus on spatial identification and optimization of ecological protection areas to enable the maintenance of specific ESs, while ignoring human development needs and their impact on ecological protection. For this reason, based on the systematic conservation planning theory, this study proposed a cost-effective multi-zoning method with multiple management zones subject to corresponding management requirements of conservation or development to achieve the sustainable maintenance of multiple ESs that are compatible or incompatible. An empirical study of the Yangtze River Economic Belt (YREB) demonstrates how this systematic multi-zoning approach may be utilized to combine five ESs and three conservation costs to prioritize the spatial distribution of management zones under different management strategies. Our findings revealed significant differences in the spatial scale and priority of various management zones determined by the spatial characteristics of the ESs and the conservation costs associated with different management strategies or scenarios. Among all scenarios, scenario 4 allows for the simultaneous achievement of protection goals for all selected ESs with minimal spaces of management zones. Further conclude that the more flexible multi-zone configuration strategy can increase the opportunities to enhance the co‐benefits of compatible ESs by allocating its conservation targets to the same function zone, and reduces the trade-offs between incompatible ESs by avoiding the allocation of conservation targets in the same function zone. This method and relevant results can provide decision-making support for the land-use sustainable management and holistic land spatial planning of the YREB.

Sustainable soil management with a correct understanding of soil properties helps to maintain soil fertility and prevent soil degradation. This research was conducted to evaluate the potentional use of soil management zones (MZs) as an efficient method to improve the productivity of wheat cultivation. According to this, 140 soil samples were taken randomly from wheat fields. Physical and chemical properties, along with topographical and spectral indices, were used to delineate MZs. Topographic attributes were extracted from digital elevation model (DEM) map preapared in SAGA GIS 7.8.2 software from the study area. Mapping the applied variables, selecting the optimal variable using principal component analysis, and delineating the study area based on soil properties, topographic attributes and spectral indices by using cluster algorithms in combination with geostatistics are major steps for delineating management zones. The fuzziness performance index (FPI) and normalized classification entropy (NCE) were investigated for the number of MZs. The semivariogram and kriging prediction maps showed different spatial distribution patterns, with spatial autocorrelation ranging varies from weak to strong for most of the applied variables. Finally, six principal components (PCs) with an eigenvalue of more than 1 and a total variance of 76.3% were chosen for further analysis. Based on the lowest values of FPI and NCE, six MZs were identified. The mean values demonstrate the difference between the applied properties in the MZs. This study showed that the delineation of MZs can be effectively used in soil management for cultivation crops to maximise crop production. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Soil health indicators can guide soil management-related decisions for sustainable agriculture. Principle component (PC) analysis and the fuzzy k-means technique, also known as continuous classification, are useful for designing site-specific management strategies for varying soil properties within a contiguous area. The objective of this study was to identify appropriate soil health indicators as well as to create contiguous areas for precision management of a large diverse farm from measured soil properties. From the farm, which is sited on Armijo–Harkey soil, 286 loose and intact samples were obtained, representing a depth of 15 cm from the soil surface. Statistical analysis showed that several data were log-normally distributed. PCA analysis showed that the first three PCs explained 73% of the variation with PC1, consisting of factors related to the soil's physical condition; PC2, containing factors related to chemical properties; and PC3, including factors related to macro- and micro-porosities. Minimizing the fuzziness performance index (FPI) and modified partition entropy (MPE) delineated four management classes. The membership class maps showed that the contrasting management strategies could be developed for the four management zones to achieve yield goals while conserving scarce surface water for irrigation, increasing water use efficiency, and decreasing nitrate leaching in arid and semi-arid irrigated farmlands. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (ECa) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of ECa sensors, namely an ECa contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and ECa data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the ECa measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of ECa to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization.

ABSTRACT The goal of this paper was to apply a method for the delineation of vegetation homogeneous zones (HZs) to map the profitability of cotton ( Gossypium sp.) using NDVI (Normalized Difference Vegetation Index) data from RPA imagery. Two irrigated cotton crops, at the FAU (103 ha) and FRP farms (106 ha), located in the western region of Bahia State, Brazil, were studied. The NDVI images were classified into three HZs: “high”, “medium” and “low” plant vigor; using the k-means clustering method. In each HZ the yield was measured, and the profitability estimated. In FAU we found that the lower the HZ’s NDVI values, the lower its profitability, because there was profit in the “high vigor” HZ, of US$ 829.40 ha-1, and loss in the “low vigor” HZ, of about US$ ‒1.256.09 ha-1 (‒251.44% compared with the “high vigor”). In the FRP plantation, the lower the NDVI values, the lower the loss of the plantation, as there were losses in all HZs: from “high” to “low vigor” HZ, the percentage difference in profitability was ‒343.43%. Thus, the use of a low-cost modified near-infrared Canon S100 on an RPA enabled the mapping of crop profitability, aiding the search for the factors behind yield variability.

Soil salinity is one of the more serious problems that cause soil degradation, particularly in semi-arid and arid regions. In agricultural lands, it threatens plant growth and productivity, which requires specific management decisions for various inputs, such as fertilizers. Accurate prediction and mapping of soil salinity have become possible through remote sensing techniques. The focus of this research is to derive soil salinity from spectral indices measured from different bands of sentinel-2 satellite in order to delineate management zones (MZs). Four spectral salinity indices were used for the purpose of this study, using blue, green, red, and NIR bands in different combinations. Exponential regression best describes the relationship between salinity indices and soil electrical conductivity (EC) through groundtruthing. The results indicate that salinity indices 2 ( B l u e × R e d G r e e n ) and 4 ( G r e e n + R e d 2 ) (SI2 and SI4) outperformed the other indices and could describe 70% and 71% of the variability in soil EC with prediction models of: S o i l E C = 0.319 e 0.0007 × S I 2 and S o i l E C = 0.3418 e 0.0006 × S I 4 , respectively. The generated models were validated by normalized root mean square error and the d coefficient of agreement. The results of the best-fit models were 16.5% and 0.86 for SI2 and 16.7% and 0.84 for SI4, respectively. The box-and-whiskers diagram proposed that the field can be divided into four soil EC-dependent groups, which are considered MZs to support a variety of management decisions. This study shows that through sentinel-2 multispectral imagery, the spatial variability of soil salinity can be mapped and used as a basis for MZs within the studied region. [ABSTRACT FROM AUTHOR]

Monitoring the climatic conditions of crops is essential for smart agriculture development and adaptation of agricultural systems in the era of global change. Thereby, it is possibly better to understand the stages of development of the crop, thus adopting management practices more efficiently and planning the harvest with greater accuracy. This study was developed to analyze the growing degree-hours and degree-days in two management zones (MZs) for each phenological stage of wheat (Triticum aestivum L.) and the application of low-cost agroclimatological stations to monitor the climatic conditions of the field production. The study was developed in a Ferralsol in Céu-Azul/Brazil. Ten low-cost agrometeorological stations were installed in two MZs delineated based on elevation data using the web platform AgDataBox. Data on solar radiation, atmospheric pressure, wind speed, precipitation, relative humidity, air, and soil temperature were evaluated over two wheat crop seasons. Our results showed different climatic conditions, especially humidity and temperature, between MZs and crop seasons, which could probably cause yield variability. By the low-cost agroclimatological stations, it is possible to collect data on the thermal accumulation by the culture in growing degree-hours, which is a more accurate parameter than the growing degree-days (commonly used in similar studies). With the growing degree-hours data, it was possible to follow the development of the phenological stages of wheat. In conclusion, the results obtained suggest the importance of evaluating agroclimatological parameters in monitoring wheat crops. However, more studies are needed in regions with greater slopes, which may have microclimates that intensely influence the crop. [ABSTRACT FROM AUTHOR]

This paper addresses a selective harvest planning problem in the context of a hierarchical agricultural supply chain, which integrates the definition of management zones for harvest planning, and the coordination between the producer and the wholesaler. The problem is represented through a stochastic bilevel program that allows the representation of the hierarchy between the producer (leader) and a wholesaler (follower). The producer decides planning and scheduling of the selective harvest for each homogeneous management zone into the resulting partition, and the wholesaler decides the amount to be acquired to satisfy demand requirements. At each decision level, a stochastic optimization model is proposed for representing the uncertainty in future crop yields, prices, and demands, using a finite set of scenarios. A reformulation of the bilevel model into a mixed‐integer linear program is provided using Karush–Kuhn–Tucker conditions and replacing the nonlinear complementary constraints allowing for the introduction of auxiliary binary variables and a big‐M term. This model was applied in a case study for selective harvesting of grapes with data collected from a farm located in the south central zone of Chile. Our research shows valuable results of the proposed methodology from a set of instances representing the behavior of both decision makers under uncertainty. [ABSTRACT FROM AUTHOR]

The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs. [ABSTRACT FROM AUTHOR]

As agriculture becomes more data driven, decision-making has become the focus of the industry and data quality will be increasingly important. Traditionally, yield data cleaning techniques have removed individual data points based on criteria primarily focused on the yield values themselves. However, when these methods are used, the underlying causes of the errors are often overlooked and as a result, these techniques may fail to remove all of the inaccurate (errorprone) data and/or remove legitimate data. In this research, an alternative to data cleaning was developed. Data integrity zones (DIZ) within each field were identified by evaluating metadata which included data collected by the combine that reported the operating conditions of the machinery (i.e., travel speed, crop mass flow), data about the field environment (i.e., soil type, topography, weather), and data of field operations (e.g., field logs, as-applied maps). Data in DIZ were isolated using buffers and the analysis of the reduced datasets was compared to the raw data. The amount of data removed depended on the amount of variability (e.g. soil characteristics, topography) in the field. Statistical comparisons of the data showed the mean yield estimates for soil type polygons increased by an average of 1.4 Mg/ha for corn when DIZ data was used compared to raw data. On average, the confidence around the mean remains similar even with a large amount (70%) of data removed. Notably, the none of the mean estimates derived from raw datasets were contained in the confidence intervals produced from DIZ data. This meta-data (context-driven) alternative to data cleaning effectively removed errors and artifacts from yield data which would only be identified when looking beyond the yield measurements themselves. When similarly reduced datasets are used to analyze historical yield data, they should provide a clearer picture of true yield effects of treatments, management zones, soil types, etc.; this will improve decisions on input and resource allocation, support wiser adoption of precision agricultural technologies, and refine future data collection. [ABSTRACT FROM AUTHOR]

This study examines the use of leaf area index (LAI) to inform variable-rate irrigation (VRI) for irrigated alfalfa (Medicago sativa). LAI is useful for predicting zone-specific evapotranspiration (ETc). One approach toward estimating LAI is to utilize the relationship between LAI and visible vegetation indices (VVIs) using unmanned aerial vehicle (UAV) imagery. This research has three objectives: (1) to measure and describe the within-field variation in LAI and canopy height for an irrigated alfalfa field, (2) to evaluate the relationships between the alfalfa LAI and various VVIs with and without field average canopy height, and (3) to use UAV images and field average canopy height to describe the within-field variation in LAI and the potential application to VRI. The study was conducted in 2021–2022 in Rexburg, Idaho. Over the course of the study, the measured LAI varied from 0.23 m2 m−2 to 11.28 m2 m−2 and canopy height varied from 6 cm to 65 cm. There was strong spatial clustering in the measured LAI but the spatial patterns were dynamic between dates. Among eleven VVIs evaluated, the four that combined green and red wavelengths but excluded blue wavelengths showed the most promise. For all VVIs, adding average canopy height to multiple linear regression improved LAI prediction. The regression model using the modified green–red vegetation index (MGRVI) and canopy height (R2 = 0.93) was applied to describe the spatial variation in the LAI among VRI zones. There were significant (p < 0.05) but not practical differences (<15%) between pre-defined zones. UAV imagery coupled with field average canopy height can be a useful tool for predicting LAI in alfalfa. [ABSTRACT FROM AUTHOR]

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management. [ABSTRACT FROM AUTHOR]

The production of crops in saline and alkali-degraded areas is difficult due to the heterogeneous and spatial variation of soil fertility. First, their spatial variability was analyzed and maps of the spatial distribution were created using Geostatistical techniques. The fuzzy k-mean clustering analysis was then used to define Management zones in the coastal saline soils of Ramanathapuram district in Tamil Nadu. One hundred and fifty geo-referenced soil samples (30 cm depth) were taken and analyzed for pH, electrical conductivity (ECe) in the saturated paste extract (USSL method), organic carbon (OC) (Walkley-Black chromic acid wet oxidation method), calcium carbonate (CaCO3) (Rapid titration method) and available phosphorus and extractable micronutrients (Multinutrients extraction method), revealing significant variation in soil characteristics throughout the coastal saline soils of Ramanathapuram district. The most significant factors, which together accounted for four principal components and 69% of the overall variability, were pH, electrical conductivity (ECe), calcium Carbonate and available zinc. According to Geostatistical analysis, the Exponential (pH, OC (organic carbon), P, Fe, Mn and Zn) and Stable (ECe) was the best fit semivariogram ordinary kriging model with weak to moderate spatial dependence. Fuzzy k-mean clustering was also used to identify zone 1, zone 2 and zone 3. For every soil property, there was a significant difference between MZ1(zone 1), MZ2(zone 2) and MZ3(zone 3). These results also showed that cluster analysis gave farmers a chance to use location-specific nutrient management strategies by minimizing variability within the zone. The management zones can decrease agricultural inputs and environmental pollutants while increasing crop productivity. [ABSTRACT FROM AUTHOR]

Variable rate irrigation (VRI) is used to save water whilst maintaining crop yields in semiarid regions. A key problem is to be able to inexpensively determine spatial patterns in soil moisture so that VRI zones can be defined. In Southern Idaho, USA, the annual precipitation is low and most fall as winter snow. This research investigates whether snow melt patterns measured using freely available time-series Sentinel 2 imagery from Google Earth Engine can define useful VRI zones for two arable fields (Grace and Rexburg). The normalized difference snow index (NDSI) was computed for each 10 m pixel with snow for all winter images of the fields for 2018–2022. NDSI values were ranked within each image and average ranks were calculated for each month and over several years. The patterns of March NDSI were most similar to patterns in yield and soil moisture observed in previous years. Zones were determined using K-means classification of the mean ranks of March NDSI. Kruskal Wallis H tests showed consistent and significant differences between zones for key soil, plant, and topographic variables. For the Grace site, differences between zones were more consistent in their order of magnitude than VRI zones which were calculated using a labor-intensive method. For the Rexburg site, zones were shown to be better when based on snowmelt data from March 2018 to 2022 rather than just March 2019. It is important to base zones on several years of data because in some years there was no snow observed in the Grace field in March. In locations where the majority of soil moisture comes from snowmelt, basing VRI zones on several years of snowmelt patterns in March is a useful and inexpensive tool for deriving meaningful VRI zones. The code used to automatically extract suitable sentinel images and calculate the NDSI is included so that practitioners can use this approach in other locations.

This study aimed to simulate dynamic irrigation management zones (MZs) in two maize fields for a variable rate hose reel fertigation machine (VRFM) with a four-section boom control. Soil moisture content was measured from nine and four soil moisture sensors in Field 1 (8.2 ha) and Field 2 (2.5 ha), respectively, on different dates during the 2022 crop season. Three and five MZs scenarios were simulated per irrigation and the theoretical maps were processed for implementation. The application maps fitted to the VRFM showed significant spatiotemporal variations in irrigation requirements. For instance, in Field 1, 3-MZ modelling showed that the areas requiring high (H), medium (M), and low (L)-level irrigation on 21 July were 1.60, 4.84, and 1.85 ha, respectively, even though the farmer applied uniform rate over the whole field. H-level sub-areas ranged between 1.22 ha (25 July) and 3.25 ha (7 July), showing a coefficient of variation (CV) of 43.32% for the three MZs, whereas H-level sub-areas for the five MZs varied from 0.41 ha (2 July) to 1.49 ha (7 July) with a CV value of 48.84%. High levels of within-field variability can be addressed using precise and dynamic irrigation MZs fitted to the irrigation technology used.

This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation.

Adoption of water-conservation irrigation practices could potentially reduce water and energy use and increase profitability, as well as protect the environment. Precision irrigation consisting of soil sensors (SS) for irrigation scheduling and variable rate irrigation (VRI) was compared with conventional uniform irrigation (URI). The study was conducted in South Alabama during the 2018 and 2019 corn growing seasons. The SS-VRI and URI treatments spanned the length of the field and were compared across five different management zones (MZ) that exhibited soil and terrain differences. Soil water tension sensors were installed on each MZ-treatment area to monitor hourly soil water changes. Results showed that on the two zones covering 55% of the study field, MZ 1 and MZ 2, the SS-VRI treatment, on a two-year average, resulted in 26% less irrigation water applied compared to the URI treatment; however, there were no statistical differences between yields or yield variability among treatments. Even though in MZ 4, there was not a substantial difference in irrigation water applied among treatments, soil sensors increased the precision of irrigation rate determination during the peak of high crop water demand. Findings from this study showed that as rainfall amount and distribution change over a crop growing period, soil sensor-based irrigation scheduling could be used to prevent over- or under irrigation. With proper management, the combination of soil sensors and VRI provides farmers with the opportunity to reduce water use, while increasing or maintaining yields; however, farmers must consider the investment and operating costs relative to the benefits. [ABSTRACT FROM AUTHOR]

Current irrigation management zones (IMZs) for variable rate irrigation (VRI) systems are static. They are delineated in the beginning of the season and used thereafter. However, recent research has shown that IMZ boundaries are transient and change with time during the growing season. The primary goal of this study was to explore the potential of using vegetation indices (VIs) developed from unmanned aerial vehicle (UAV) and satellite images to predict cotton physiological parameters that can be used to delineate in-season boundaries of IMZs. A 2 year study was conducted in a 38 ha commercial cotton field in southwestern Georgia, USA. Throughout the two growing seasons, VIs were calculated from UAV, PlanetScope, and Sentinel-2 images. Predawn leaf water potential (LWPPD) and plant height were measured at 37 locations in the field on the same day as the flights and correlated with UAV and satellite based-VIs. GNDVI (Green normalized difference vegetation index) was the best predictor of plant height with correlation values of 0.72 (p <.0001) and 0.84 (p <.0001) for 2019 and 2020, respectively. A secondary goal was to compare the performance of dynamic VRI (DVRI) to conventional irrigation. The field was divided into alternating parallel conventional, and DVRI strips to compare the two scheduling methods. The conventional strips were irrigated using the farmer's standard method and individual IMZs within the DVRI strips were irrigated based on soil water tension (SWT) measured with a wireless soil moisture sensor network. LWP and SWT measurements correlated well. IMZs were initially delineated using soil texture, apparent soil electrical conductivity (ECa), and yield maps and satellite images from previous years and were modified in-season to reflect patterns observed in the plant height maps. In 2020, the DVRI system prescribed an average irrigation amount of 50.8 mm while conventional irrigation applied an average of 58.4 mm. Average yields for DVRI and conventional were 1248 and 1191 kg ha−1, respectively. The DVRI system resulted in average yield 4.6% higher than conventional irrigation, while applying 14.0% less water. Despite the lower water application by the DRVI system, the performance comparison between the DRVI and the conventional irrigation was not conclusive. [ABSTRACT FROM AUTHOR]

Oil palm (Elaeis guineensis Jacq.) cultivation is said to have caused losses of soil carbon due to deforestation. However, current oil palm cultivation is carried out either on previously rubber and cocoa plantation or replanted on first-or second-generation oil palm. The present study was conducted to assess whether there is a build-up of soil carbon throughout the growth of oil palm and will those amassed carbon (if any) be lost during replanting. The study was conducted at oil palm ages 5, 10, 15 years old, and newly replanted oil palm. At each age, soils were sampled at the frond heap pile (FH), the harvesting path (HP), and the inter-row (IR). Soil carbon content at all plots was not significantly different between ages with a mean of 2.24%. Between sections, soil carbon content at FH (3.10±1.42%) was significantly higher than the other sections. Our results showed that age of oil palm did not influence the accumulation of soil carbon. Replanting was also found not to have caused losses of soil carbon. As the different sections of the plantation yielded different results, future measurements of soil carbon should consider these different sections to properly represent the whole plantation. [ABSTRACT FROM AUTHOR]

• This study evaluated the feasibility of accurately predicting corn (Zea mays) yield at the management zone level. • Data fusion of visible and near-infrared spectroscopy-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms was used. • A clustering analysis was used to develop MZs in order to implement variable-rate nitrogen fertilization in a drip-irrigated corn field. • The developed models demonstrated moderate accuracy for corn yield prediction. • Further research should include supplementary spectral crop canopy indices and alternative deep and machine learning approaches. The integration of advanced technologies, such as soil proximal sensing, remote sensing, and machine learning, has revolutionized agricultural practices, particularly for corn yield prediction. This interdisciplinary approach harnesses the power of cutting-edge sensors to gather high-resolution data on soil conditions coupled with remote sensing technologies that provide a comprehensive view of crop health and environmental factors. This study aimed to evaluate the feasibility of accurately predicting corn (Zea mays) yield at the management zones (MZs) level using the fusion of visible and near-infrared spectroscopy (Vis-NIRS)-derived soil properties, remote sensing-derived crop spectral indices, and machine learning algorithms. Clustering analysis was used to develop MZs to implement variable-rate nitrogen fertilization (VRNF) in a drip-irrigated corn field. Site-specific models to forecast corn yield at the MZs level were developed using Sentinel 2A-derived spectral indices and machine learning regression algorithms. Partial least squares Vis-NIR spectral regression modelling for MZs development achieved high accuracy in terms of the coefficient of determination (R2) which was ranged from 0.60 to 0.99 in cross-validation and from 0.52 to 0.78 in online validation. The developed corn yield prediction models demonstrated moderate efficacy, as evidenced by the R2 values ranging from 0.50 to 0.71. Further research should include supplementary spectral crop canopy indices and the application of alternative deep and machine learning approaches to improve the accuracy of the prediction models. [ABSTRACT FROM AUTHOR]

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.

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In this study, three digital, site-specific, yield-mapping methods for winter wheat were examined, and their precision was evaluated. The crop yields of heterogeneous fields at three locations were determined on a site-specific basis using a yield-recording system composed of a combine harvester and algorithms based on reflection measurements made via satellites, as well as a tractor-mounted sensor. As a reference, the yield was determined with a plot harvester (ground truth data). The precision of the three methods was evaluated via statistical indicators (mean, median, minimum, maximum, and standard deviation) and correlation analyses between the yield of the ground truth data and the respective method. The results show a yield variation of 4.5–10.9 t ha−1 in the trial fields. The yield of the plot harvester was strongly correlated with the yield estimate from the sensor data (R2 = 0.71–0.75), it was moderately correlated with the yield estimate from the satellite data (R2 = 0.53–0.68), and it ranged from strongly to weakly correlated with the yield map of the combine harvester (R2 = 0.30–0.72). The absolute yield can be estimated using sensor data. Slight deviations (<10%) in the absolute yield are observed with the combine harvester, and there are clear deviations (±48%) when using the satellite data. The study shows differences in the precision and accuracy of the investigated methods. Further research and optimization are urgently needed to determine the exactness of the individual methods. [ABSTRACT FROM AUTHOR]

Precision fertilization implies the need to identify the variability of soil fertility, which is costly and time-consuming. Remotely measured data can be a solution. Using this strategy, a study was conducted, in a vineyard, to delineate different management zones using two indicators: apparent soil electrical conductivity (ECa) and normalized difference vegetation index (NDVI). To understand the contribution of each indicator, three scenarios were used for zone definition: (1) using only NDVI, (2) only ECa, or (3) using a combination of the two. Then the differences in soil fertility between these zones were assessed using simple statistical methods. The results indicate that the most beneficial strategy is the combined use of the two indicators, as it allowed the definition of three distinct zones regarding important soil variables and crop nutrients, such as soil total nitrogen, Mg2+ cation, exchange acidity, and effective cation exchange capacity, and some relevant cation ratios. This strategy also allowed the identification of an ionic unbalance in the soil chemistry, due to an excess of Mg2+, that was harming crop health, as reported by NDVI. This also impacted ECa and NDVI relationship, which was negative in this study. Overall, the results demonstrate the advantages of using remotely sensed data, mainly more than one type of sensing data, and suggest a high potential for differential crop fertilization and soil management in the study area. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic, machine learning methodology for precision agriculture, aiming at learning management zones that allow a more efficient and sustainable use of fertiliser. In particular, the methodology consists of clustering remote sensing data and estimating the impact of decision-making based on the extracted knowledge. A case study is developed on experimental data coming from winter wheat (Triticum aestivum) crops receiving site-specific fertilisation. A first approximation to the data allows measuring the effects of the fertilisation treatments on the yield and quality of the crops. After verifying the significance of such effects, clustering analysis is applied on sensor readings on vegetation and soil electric conductivity in order to automatically learn the best configuration of zones for differentiated treatment. The complete methodology for identifying management zones from vegetation and soil sensing is validated for two experimental sites in Denmark, estimating its potential impact for decision-making on site-specific N fertilisation. [ABSTRACT FROM AUTHOR]