Site-specific Management Zones Research Articles

Delineation of Management Zones for Site-Specific Management of Potato Crop in Some Areas in Western Nile Delta, Egypt

Delineation of Management Zones for Site-Specific Management of Potato Crop in Some Areas in Western Nile Delta, Egypt

Enhancing Irrigation Management: Unsupervised Machine Learning coupled with Geophysical and Multispectral Data for Informed Decision-Making in Rice Production

Integrating diverse data sources for site-specific management zones (SSMZ) in precision agriculture is a complex task. Soil surveys using apparent electrical conductivity (ECa) have proven effective in capturing field variability. However, relying solely on one sensing data type may not fully capture the intricate relationship between soil properties and crop physiological processes. This research assessed the effectiveness of applying multivariate geostatistical analysis and unsupervised machine learning (UML) to geophysical and multispectral data through ECa, NDWI and NDVI indices, for delineating and validating the SSMZ at different crop cycles in five rice field of Tolima department-Colombia. Lee´s correlation was conducted between ECa, NDWI and NDVI data. MULTISPATI-PCA was used to assess the autocorrelation in the input variables before SSMZ delineation. The correlation results revealed two field categories based on the direct or indirect relationship between ECa and the multispectral data. On one hand, ECa values showed an inverse correlation (p<0.01) with multispectral data (-0.25 to -0.45) for fields 1a, 1b and 4a. On the other hand, there was a direct correlation (p<0.01) between ECa and multispectral data (0.31 to 0.51) for fields 2b and 5a. For all rice fields, MULTISPATI-PCA explained a range between 75.01 % to 95.64 % of the total variability. UML results highlighted two, three and four SSMZ, with two SSMZ being the optimal management zones number. The validation suggests that the θ data showed a significant statistical difference for two SSMZs (p < 0.05), regardless of the water content ranges. This study emphasized the importance of integrating geophysical and multispectral datasets in precision agriculture for evaluating field variability and crop conditions.

Evaluation of Variable Application Rate of Fertilizers Based on Site-Specific Management Zones for Winter Wheat in Small-Scale Farming

China is currently experiencing a severe issue of excessive fertilization. Variable rate fertilization (VRF) technology is key to solving this issue in precision agriculture, and one way to implement VRF is through management zone (MZ) delineation. This study is aimed at evaluating the feasibility and potential benefits of VRF based on site-specific MZs in smallholder farm fields. This study determined the amounts of basal and top-dressing fertilizers in different spatial units, based on soil nutrient MZs and crop growth MZs, respectively. The potential agronomic, economic, and environmental advantages of spatial variable rate fertilization were further assessed by comparing the farmer’s treatment, the expert’s treatment, and the variable rate fertilization treatment based on management zones (VR-MZ). The results showed that VR-MZ reduced the use of nitrogen (N), phosphorus (P), and potassium (K) fertilizers by 22.90–43.95%, 59.11–100%, and 8.21–100%, respectively, and it also increased the use efficiency of N, P, and K by 12.27–28.71, 89.64–176.85, and 5.48–266.89 kg/kg, respectively, without yield loss. The net incomes of VR-MZ were 15.5–449.61 USD ha−1 higher than that of traditional spatially uniform rate fertilization. Meanwhile, less nitrous oxide emission (23.50–45.81%), ammonia volatilization (19.38–51.60%), and nitrate ion leaching amounts (28.77–53.98%) were found in VR-MZ compared to those in uniform fertilization. The results suggest that the VR-MZ has great potential for saving fertilizers, significantly increasing farmers’ net income, reducing environmental pollution, and promoting the sustainable use of resources. This study provides a theoretical basis and technical support for exploring a VRF suitable for village-scale farming.

Using analytical hierarchy process to determine intra-field heterogeneity zones upon implementation of precision farming

The study aimed to develop a methodology for determining zones of intra-field heterogeneity for precision farming. In this study, we took into account the Belarussian national land use system which provides for the absence of private ownership of agricultural land. The spatial distribution of intra-field heterogeneity zones within the land use area of 7549.49 thousand hectares was identified using the analytical hierarchy process (AHP). The algorithm for determining zones of spatial heterogeneity provides for: (1) the selection of indicators and their ranking; (2) developing a pairwise comparison matrix, (3) estimating relative weights and (4) assessing matrix consistency. It is recommended to use data from agrochemical soil studies which are conducted centrally every 4 years for each agricultural enterprise as input parameters. These data include the humus content in the soil, the content of available phosphorus and potassium, soil pH, and the content of B, Cu, Zn, Ca, and Mg. The data should be carefully examined using spatial statistics tools to provide a more accurate delineation of the management-zones boundaries. The developed technique makes it possible to determine fertile and marginal areas within each field and differentiate the use of fertilizers, taking into account the presence of intra-field heterogeneity. This will reduce the total cost of purchasing and applying phosphorus fertilizers by 34 $·ha-1 and potash fertilizers by 9 $·ha-1 due to the redistribution of the fertilizer dose calculated for the planned yield, taking into account the identified site-specific management zones. At the same time, the level of chemical pressure per hectare of arable land will decrease by 6.7% without loss of crops productivity.

Towards site specific management zones delineation in rotational cropping system: Application of multivariate spatial clustering model based on soil properties

Site|specific management for irrigation purposes remains a great challenge especially when dealing with mixed cropping systems due to spatial variability of soil properties. This variability generates crop yield heterogeneity within agricultural fields.In this study, rice-corn, cotton rotational cropping system was cultivated in Tolima region-Colombia. A total of 72 geo-referenced representative surface soil samples (0–20 cm depth) were collected covering a total area of 5 ha of Inceptisols. The aim was to delineate field management zones (MZs) using three methods M1, M2 and M3, and evaluate their usefulness for a further site-specific management. Georeferenced soil samples were analyzed for soil texture, bulk density (BD), field capacity (FC), soil available water (AW), mesopores percentage (Mes), soil penetration resistance (PR), organic matter (OM), available phosphorus (P), and exchangeable bases content (Ca + Mg/K). Spatial variability of soil properties was analyzed with geostatistics approach. Further, spatial principal component (sPC), were performed to delineate the MZs using: M1- Fisher-Jenks algorithm with the first sPC; M2- Fuzzy k-means cluster analysis with two sPC; and M3- Fuzzy k-means with soil variables. MZs delineation results were validated by comparing the zones to soil properties and yield data coming from the rice-corn, cotton system. Two MZs were recommended using M2 due to its lower fragmentation compared to other methodologies. This delineation could be suggested for fertilization management since Clay + Silt, OM, P, and Ca + Mg/K content differentiate when delineating one and two MZs. M2 provided differences for AW, Mes, and PR when considering three MZs which can be used as a basis for irrigation site-specific management in precision agriculture.

Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil

Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p &lt; 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca2+, Mg2+, SB, Al3+, H+ + Al3+, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca2+, Mg2+, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs according.

Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India

Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India

Mapping Soil Properties with Fixed Rank Kriging of Proximally Sensed Soil Data Fused with Sentinel-2 Biophysical Parameter

Soil surveys with line-scanning platforms appear to have great advantages over the traditional methods used to collect soil information for the development of field-scale soil mapping and applications. These carry VNIR (visible and near infrared) spectrometers and have been used in recent years extensively for the assessment of soil fertility at the field scale, and the delineation of site-specific management zones (MZ). A challenging feature of VNIR applications in precision agriculture (PA) is the massiveness of the derived datasets that contain point predictions of soil properties, and the interpolation techniques involved in incorporating these data into site-specific management plans. In this study, fixed-rank kriging (FRK) geostatistical interpolation, which is a flexible, non-stationary spatial interpolation method especially suited to handling huge datasets, was applied to massive VNIR soil scanner data for the production of useful, smooth interpolated maps, appropriate for the delineation of site-specific MZ maps. Moreover, auxiliary Sentinel-2 data-based biophysical parameters NDVI (normalized difference vegetation index) and fAPAR (fraction of photosynthetically active radiation absorbed by the canopy) were included as covariates to improve the filtering performance of the interpolator and the ability to generate uniform patterns of spatial variation from which it is easier to receive a meaningful interpretation in PA applications. Results from the VNIR prediction dataset obtained from a pivot-irrigated field in Albacete, southeastern Spain, during 2019, have shown that FRK variants outperform ordinary kriging in terms of filtering capacity, by doubling the noise removal metrics while keeping the computation cost reasonably low. Such features, along with the capacity to handle a large volume of spatial information, nominate the method as ideal for PA applications with massive proximal and remote sensing datasets.

Mapping of Soil Nutrient Variability and Delineating Site-Specific Management Zones Using Fuzzy Clustering Analysis in Eastern Coastal Region, India

Mapping of Soil Nutrient Variability and Delineating Site-Specific Management Zones Using Fuzzy Clustering Analysis in Eastern Coastal Region, India

Technical Note: A Comparison of Supervised Machine Learning Algorithms for Predicting Subfield Yield Variability of Maize Grain

Abstract. Precision farming is about managing within-field spatial variability by applying appropriate inputs with the right amount, at the right place, and at the right time. This typically leads to the delineation of site-specific management zones that represent different yield potentials. However, a focus on spatiotemporal interactions is generally lacking. This technical note explores the viability of predicting spatial yields within fields, in the small-field arable production commonly practiced in New Zealand, using supervised machine learning algorithms. The methods used are multiple linear regression (MLR), feed-forward backpropagation neural network (FFNN), classification and regression tree (CART), random forest (RF), XGBoost, and Cubist, using predictors compiled from a range of spatiotemporal factors, including soil electrical conductivity (EC), soil organic matter (OM), elevation, rainfall, growing degree days, and solar radiation. Despite poor results (R2 = 0.06 to 0.50) for predicting the spatial yield of each year, the RF, XGBoost, and Cubist models demonstrated greater capability for modeling spatiotemporal interactions than the previously tested FFNN and MLR. The inclusion of consistently calibrated yield data for additional years and more related variables (e.g., soil moisture and canopy cover) could improve the models. The results of these modeling analyses could lead to the delineation of dynamic yield management zones for improving the precision of mid-season fertilizer prescriptions to improve yield. Keywords: GIS, Machine learning, Precision farming, Spatiotemporal.

The Earth Observation-based Anomaly Detection (EOAD) system: A simple, scalable approach to mapping in-field and farm-scale anomalies using widely available satellite imagery

To feed the world increasing population, expansion in the area under arable cultivation is expected, with the majority projected to occur in Sub-Sahara Africa and Latin American countries. However, many existing Precision Agriculture (PA) techniques are difficult to transfer to agricultural systems in these regions as they rely on prohibitively expensive crop monitoring systems. Satellite Earth Observation (EO) has the ability to provide affordable solutions, particularly to identify yield-limiting conditions within site-specific management zones (MZs). This paper presents the Earth Observation-based Anomaly Detection (EOAD) approach, a novel system for the detection of in-field anomalies through automatic thresholding of optical Vegetation Index data, based on their deviation from a normal distribution. The EOAD sets dynamic thresholds for the pixel values within a parcel by removing the atypical values in increments from the tails towards the median until the distribution is normal. The distribution normality is assessed based upon measures of skewness and kurtosis for each iteration. The anomaly detection approach demonstrated a strong agreement, 80% overall accuracy, with identified in-field anomalies when applied to rice plots in the Ibague Plateau, Colombia, using both Sentinel-2 and PlanetScope imagery. Areas identified as anomalous during the booting stage were shown to be significantly (p ⩽0.005) associated with a decrease in final yield. Additionally, the percentage of anomalies detected with the EOAD improved the detection of underperforming plots in early growth stages. Using freely available data and software, this automated approach demonstrates an exciting potential for use in improving agricultural practices in low-resource regions.

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.

Site-specific management zones based on geostatistical and fuzzy clustering approach in a coastal reclaimed area of abandoned salt pan

Site-specific management zones based on geostatistical and fuzzy clustering approach in a coastal reclaimed area of abandoned salt pan

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones

DELINEATION OF SITE-SPECIFIC MANAGEMENT ZONES USING MULTIVARIATE ANALYSIS AND GEOGRAPHIC INFORMATION SYSTEM TECHNIQUE

The basic objectives of this study were: 1) The ability to characterize the spatial variability across a soil for selected soil properties using GIS technique and 2) identification of site-specific management zones using selected soil properties using PCA and cluster analysis. 120 geo-referenced representative soil samples obtained from Sahl AlHussainiyah, El-Sharkia Governorate, Egypt, (from 0 to 0.60 m depth). These samples were prepared and analysed for soil characteristics, such as soil pH, electrical conductivity (ECe), calcium carbonate (CaCO3 ), soil organic matter (OM), available N, P, K, soil cations exchange capacity (CEC), and bulk density (BD). Using semi-variogram analysis and ordinary kriging, spatial distribution pattern varies from moderate to strong spatial dependence for most soil characteristics. Using PCA and cluster analysis, site-specific management zones were identified in the study area. For further analysis, four PCs with eigenvalues &gt; 1 were used, with PCs explaining 73.19 percent of the variance. Four MZs were defined based on cluster analysis using an agglomerative hierarchical clustering technique. The differences between these MZs were statistically significant (p &lt; 0.05). 3.01 (466.56 ha), 36.47 (5658.9 ha), 31.02 (4813.54 ha), and 29.5 percent (4577.8 ha) of the total area is MZ1, MZ2, MZ3, and MZ4 (15516.8 ha).

Delineation of site‐specific management zones using estimation of distribution algorithms

AbstractIn this paper, we present a novel methodology to solve the problem of delineating homogeneous site‐specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objective is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chemical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which generates zones with orthogonal shapes, for example, L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real field and randomly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricultural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.

Taking into account change of support when merging heterogeneous spatial data for field partition

The paper describes a geostatistical approach for combining multi-source data with different support for field delineation into homogeneous soil zones. It takes into account change of support explicitly given the critical influence of spatial resolution on the statistical characteristics of estimates. Geophysical and hyperspectral data were used in combination with soil chemical properties measured in the laboratory on 50 samples collected in a field cropped with Tomato (Solanum lycopersicum, L. cv San Marzano). The approach consisted in performing Gaussian anamorphosis with support correction, multi-collocated block cokriging and factorial block cokriging to jointly analyse all data. Two regionalised factors at different spatial scales were retained to split the field into homogeneous zones for site-specific management. The results emphasize the impact of spatial scale on site-specific management.

Zoning of a Newly-Planted Vineyard: Spatial Variability of Physico-Chemical Soil Properties

Soil properties show a high spatio-temporal variability, affecting productivity and crop quality within a given field. In new vineyard plantations, with changes in the initial topographic profile, this variability is exacerbated due to the incorporation of soil from different origins and qualities. The aim of the current study was to characterize the variability of soil properties in a newly established vineyard, and delineating zones for site-specific management of fertilization. For this purpose, the soil apparent electrical conductivity (ECa) in the first 150 cm was measured with an electromagnetic induction sensor. A soil sampling was performed following a regular grid (35 × 35 m, 149 samples), collecting samples down to 40 cm depth for determining soil chemical properties. Spatial variability was assessed through semivariogram calculation and ordinary kriging. The soil properties that better represent the variability in this newly established vineyard were pH, effective cation exchange capacity (ECEC), carbon content, clay and ECa. The ECa was homogeneous all over the vineyard, except for the area closer to the river where a greater human intervention had occurred, with contributions of external soil at a greater depth. Soil properties showed a great spatial variability. Interpolated maps allowed for detecting areas with a lack of nutrients in which a differential fertilization could be performed in search of a sustainable and balanced production. The information provided by the maps of pH, ECEC and carbon and potassium contents allow for performing a differential management of the vineyard in terms of fertilization. In addition, the results obtained suggest that the vineyard should be divided into two sectors for a differential irrigation management. The ECa was not significantly correlated to most of the soil properties determined in the current study; however, it allowed for a low-cost mapping of the vineyard soil and established large areas of management within the vineyard.

Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US

Nitrogen (N) and water continue to be the most limiting factors for profitable maize (Zea Mays L.) production in the western US Great Plains. Precision application of N and water has the potential to significantly enhance input use efficiency without impairing yields. The overall objective of this study was to determine the most productive and efficient nitrogen and water management strategy for irrigated maize by using site-specific management zones and a proximal remote sensing approach. This study was conducted over 2016, 2017, 2018 and 2019 crop growing seasons near Fort Collins, Colorado, USA. Six nitrogen rates (0, 56, 112, 168, 224, and 280 kg N ha−1) were applied along experimental strips across three delineated management zones (low, medium, and high productivity). Four rates of irrigation were applied to maize (60%, 80%, 100%, and 120% of evapotranspiration) using a center pivot precision irrigation system equipped with zone control. Optical proximal sensor readings were acquired on all experimental strips four times during the growing season to assess four nitrogen management strategies (uniform, management zone (MZ), remote sensing (RS), and management zone remote sensing (MZRS)) on grain yield and nitrogen use efficiency (NUE). Results from this three-year study showed the significant interaction (p = 0.05) of zone vs. irrigation and irrigation vs. nitrogen across all years. In two of the three years (2016 and 2018), the high productivity zone benefitted from high irrigation rates, demonstrated by 16% and 18% yield increase from the lowest irrigation rate. In 2016, yield plateau was reached at 168 kg N ha−1 with 80% and 100% irrigation rates, whereas the plateau was reached at 112 kg N ha−1 in the 120% irrigation rate. These results demonstrate the possibility of fine-tuning zones, irrigation, and nitrogen to achieve optimum yield. While uniform and MZ nitrogen management strategies produced the highest grain yield, the best NUE was achieved via the RS strategy, followed by the MZ strategy. In this study, the MZRS strategy (combined MZ and RS) did not produce superior yield and NUE as compared to uniform and other strategies. However, there is a tremendous opportunity to fine-tune these two strategies, using other algorithms that are not explored in this study to improve the sustainability of maize production under irrigated conditions.

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms

Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning (ML) algorithms for the extraction of useful information responsible for controlling crop yield. Four ML algorithms, namely linear regression (LR), elastic net (EN), k-nearest neighbor (k-NN), and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing. Six fields in Atlantic Canada including three fields in Prince Edward Island (PE) and three fields in New Brunswick (NB) were sampled, over two (2017 and 2018) growing seasons, for soil electrical conductivity, soil moisture content, soil slope, normalized-difference vegetative index (NDVI), and soil chemistry. Data were collected from 39–40 30 × 30 m2 locations in each field, four times throughout the growing season, and yield samples were collected manually at the end of the growing season. Four datasets, namely PE-2017, PE-2018, NB-2017, and NB-2018, were then formed by combing data points from three fields to represent the province data for the respective years. Modeling techniques were employed to generate yield predictions assessed with different statistical parameters. The SVR models outperformed all other models for NB-2017, NB-2018, PE-2017, and PE-2018 dataset with RMSE of 5.97, 4.62, 6.60, and 6.17 t/ha, respectively. The performance of k-NN remained poor in three out of four datasets, namely NB-2017, NB-2018, and PE-2017 with RMSE of 6.93, 5.23, and 6.91 t/ha, respectively. The study also showed that large datasets are required to generate useful results using either model. This information is needed for creating site-specific management zones for potatoes, which form a significant component for food security initiatives across the globe.