Delineation Of Management Zones Research Articles

A GIS-based approach to identify the spatial variability of salt affected soil properties and delineation of site-specific management zones: A case study from Egypt

1. Abdi, H., Williams, L. J., 2010. Principal component analysis. John Wiley & Sons, Inc., 2, 433-459. https://doi.org/10.1002/wics.1.... CrossRef Google Scholar

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.

Assessing the effect of spatial resolution on the delineation of management zones for smallholder farming in southern Brazil

Remote sensing estimates of cover-crop aboveground biomass (AGB) have been used to delineate management zones for smallholder farming in southern Brazil. In this study, we investigated the spatial resolution influence on the AGB estimates of rye, calculated from regression relationships with the Normalized Difference Vegetation Index (NDVI), and on the subsequent delineation of management zones using the Management Zone Analyst (MZA) software. Data acquired by an Unmanned Aerial Vehicle (UAV) Parrot Sequoia camera (0.20 m spatial resolution) in Brazil were compared with observations from the PlanetScope (PS) satellite constellation (3 m) and the Operational Land Imager (OLI)/Landsat-8 (30 m). A three-endmember mixture model (green vegetation, soil, and shadow) was applied to surface reflectance data of these instruments for evaluating the cover-crop development at two dates in August 2017. Because of the differences in the technical specifications of the sensors, we resampled the UAV dataset into four levels of spatial resolution (1, 3, 10, and 30 m). Using the UAV map (0.20 m) as a reference, we obtained confusion matrices for the original and resampled data. The results showed that the increasing amounts of rye AGB from the beginning to the end of August promoted significant changes in surface reflectance and in soil-green vegetation fractions calculated at variable spatial resolution. The performance of the regression models to estimate cover-crop AGB was approximately similar in the transition from the sub-metric (0.20 m) to the metric (3 m) spatial resolutions, or from the UAV camera to the PS data. For all datasets, the MZA detected two management zones with zone 2 having higher cover-crop AGB than zone 1. When compared to the UAV management zone map (reference), the PS map had a moderate-to-substantial agreement, while the OLI/Landsat-8 map had a fair-to-moderate concordance. Substantial agreements with the reference map were observed at simulated 1 m and 3 m data, as indicated by Kappa coefficients of 0.73 and 0.63 and overall accuracies of 86.40% and 81.40%, respectively. We conclude that the 3 m spatial resolution data of the PS comprise an alternative to delineate management zones for smallholder farming in southern Brazil when compared to the very-high spatial resolution observations of the UAV cameras.

Dynamic Management Zones for Irrigation Scheduling

Irrigation scheduling decision-support tools can improve water use efficiency by matching irrigation recommendations to prevailing soil and crop conditions within a season. Yet, little research is available on how to support real-time precision irrigation that varies within-season in both time and space. We investigate the integration of remotely sensed NDVI time-series, soil moisture sensor measurements, and root zone simulation forecasts for in-season delineation of dynamic management zones (MZ) and for a variable rate irrigation scheduling in order to improve irrigation scheduling and crop performance. Delineation of MZ was conducted in a 5.8-ha maize field during 2018 using Sentinel-2 NDVI time-series and an unsupervised classification. The number and spatial extent of MZs changed through the growing season. A network of soil moisture sensors was used to interpret spatiotemporal changes of the NDVI. Soil water content was a significant contributor to changes in crop vigor across MZs through the growing season. Real-time cluster validity function analysis provided in-season evaluation of the MZ design. For example, the total within-MZ daily soil moisture relative variance decreased from 85% (early vegetative stages) to below 25% (late reproductive stages). Finally, using the Hydrus-1D model, a workflow for in-season optimization of irrigation scheduling and water delivery management was tested. Data simulations indicated that crop transpiration could be optimized while reducing water applications between 11 and 28.5% across the dynamic MZs. The proposed integration of spatiotemporal crop and soil moisture data can be used to support management decisions to effectively control outputs of crop × environment × management interactions.

Characterization of Spatial Variability of Soil Parameters in Apple Orchards of Himalayan Region Using Geostatistical Analysis

ABSTRACT Knowledge of spatial variability in soil properties is critical for effective nutrient management plan in crop production. Poor productivity of apple orchards in Indian Himalayan Region (IHR) is due to lack of information on the variability of soil properties important for enhancing productivity in the region. The study was conducted in apple orchards with the hypothesis that spatial variability of soil properties is high due to adoption of varied management and passive soil factors. The major objectives of the study were to assess the spatial variability of soil parameters, viz. soil pH, electrical conductivity, soil organic carbon along with plant available soil nitrogen (N), phosphorus (P), potassium (K), exchangeable calcium (Ca), and exchangeable magnesium (Mg) at a regional scale through geostatistical methods. Coefficient of variation (CV) indicated that the fertility parameters varied from medium to high in heterogeneity (CV > 20%). Available N was found critical/medium in 69.6% of soil samples and might be one of the limiting nutrients for crop growth, P and K were in high, and OC in very high range. Significant correlation was found between OC with N; pH with K, Ca and Mg and EC with OC, P and K. The semivariogram parameters indicated that the spatial distribution of soil fertility parameters were inconsistent and showed strong to weak degree of spatial dependence for all parameters. The study highlighted the importance of delineation of soil fertility management zones in the apple growing region as a guide for precise and site-specific nutrient.

Site-specific Nutrient Management Zones in Soybean Field Using Multivariate Analysis: An Approach Based on Variable Rate Fertilization

ABSTRACTDelineation of site-specific nutrient management zones (MZ) provides a basis for practical and cost-effective management of spatial soil fertility in precision agriculture. Therefore, the objective of this study was the delineation of MZs in a soybean field using geostatistics, principal component analysis (PCA), and the fuzzy k-means algorithm. The study was carried out in a field with 204 ha located in São Desidério, western Bahia state, Brazil (12 ° 25 ‘ 12” S, 45 ° 29ʹ 46” W). To do so, samples of soil attributes (0–20 cm), soybean yield, electrical conductivity (EC) at 0.20 m (EC02), 0.50 m (EC05), 1.00 m (EC1), 2.00 m (EC2) soil profile depth, and the Normalized Difference Vegetation Index (NDVI) were obtained in 204 points (100 x 100 m grid). After soil sampling and laboratory analyzes, the data were submitted to descriptive statistics and a Spearman correlation analysis was performed to select those attributes related to soybean yield. Then, the spatial variability of these attributes was assessed and spatial distribution maps were constructed using geostatistical tools. Next, PCA and fuzzy k-means algorithm were then performed to delineate MZs. Finally, the agreement between the MZs maps obtained from the PCA and soybean yield was assessed using the Kappa index. Results showed that the optimal number of MZs was two, which resulted in a Kappa index of 0.61 (very good). Moreover, the analysis of variance indicated heterogeneity between all attributes analyzed in the MZs. Finally, the defined MZs provide a basis of information for site-specific nutrient management.

Comprehensive assessment of spatial soil variability related to topographic parameters in sugarcane fields

Landscapes are intrinsically related to spatial soil variability. Understanding soil chemical fertility based on topographic parameters is essential to ensuring sustainable agronomic management through the rational use of inputs. The aim of this study was to perform a comprehensive assessment of soil organic matter (OM), pH, sum of bases (SB) and cation exchange capacity (CEC) according to topographic parameters, with the goal of identifying spatial patterns and better understanding the relationship between them to capture the soil variability in sugarcane fields. A soil dataset from nine sugarcane experimental fields was evaluated. Approximately 3000 soil samples collected between 2008 and 2017 were evaluated. The topographic parameters of vertical and horizontal curvatures were related to the variability in the soil attributes. The results showed that the horizontal morphometric classes (HConv, HPlan and HDiv) associated with vertical concave areas (VConc) presented higher levels of OM, SB and CEC, which indicated that these areas have higher soil chemical fertility. The VConcHDiv showed the highest soil chemical fertility; regardless of site, topographic level and inherent soil fertility. For all vertical morphometric classes (VConc, VRet and VConv), soil pH levels were higher in horizontal divergent areas (HDiv) and lower in convergent areas (HConv), suggesting that stricter soil acidity management was needed in the HConv areas. The VConvHConv areas, where lower soil chemical fertility was observed, should be sampled with greater accuracy for adequate spatial soil characterization because it had a higher coefficient of variation (CV) than the other morphometric classes. The results showed that the detected spatial patterns were temporally stable. With high spatial and temporal stability, topographic parameters could be excellent sources of economically feasible and easily assessed information to support farmers in soil chemical fertility characterization. Finally, topographic attributes proved to be an excellent source about soil spatial variability for farmers to use in their fields aiming a site-specific management or an additional layer of data for management zones delineation.

Guiding soil sampling strategies using classical and spatial statistics: A review

AbstractSoil analysis is a key practice to increase the efficiency of nutrient management in agriculture. Since the early 20th century, increasingly sophisticated methods have been developed to describe and manipulate the inherent spatial variability in soil chemical properties within the realms of classical and spatial statistics. In this paper, we reviewed design‐based (classical) and model‐based (geostatistical) sampling to suggest field‐scale sampling strategies consistent with common agronomic management goals in annual crop production systems. To assess the relevance of common sampling methods in relation to practice, current extension recommendations across the United States were compared with results from peer‐reviewed literature. Despite decades of research, specific recommendations for sample sizes, sampling depths, numbers of soil cores, and layouts were highly variable for classical and geostatistical approaches. Mobile nutrients, such as NO3, are frequently lacking in spatial structure and rarely are recommended for site‐specific management. Nonmobile nutrients, such as P, are more spatially dependent and exhibit nested spatial structures that are inconsistent across fields. For these reasons, we recommend design‐based sampling in most situations for simplicity, cost, and objectivity. The common design‐based sampling protocol prescribes collection of individual cores in a zig‐zag pattern that are combined to produce a composite sample. This protocol should be amended because it is not sufficiently randomized and is inadequate for log‐normally distributed variables. To facilitate site‐specific management, we recommend structured approaches for delineating management zones or strata and for researchers to systematically enumerate confounding variables while explicitly defining the scope of inference for future soil sampling studies.

Soil Properties Spatial Variability and Delineation of Site-Specific Management Zones Based on Soil Fertility Using Fuzzy Clustering in a Hilly Field in Jianyang, Sichuan, China

Avoiding soil degradation and improving crop productivity could be achieved by performing sustainable soil nutrient management with an appropriate understanding of soil properties’ spatial variability. The present fertilizer recommendations for the region where the study area is located are typically symmetric for large regions. This leads to the under-application of fertilizers in zones with low nutrient contents and over-application in zones with high nutrient contents. Therefore, this study was conducted to assess soil management zones (MZs) in the study area for effective soil nutrient management and to evaluate soil properties’ spatial variability. A total of 100 geo-referenced soil samples were collected at a depth of 0–20 cm, processed and analyzed for pH, available nitrogen (AN), available phosphorus (AP), available potassium (AK), soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP), while C:N, C:P and N:P ratios were calculated. Soil properties’ coefficients of variation (CVs) widely varied from low (1.132%) to moderate (45.748%). Ordinary kriging and semi-variogram analysis showed differed spatial variability patterns for the studied soil properties with spatial dependence ranged from weak to strong. MZs were delineated by performing principal component analysis (PCA) and fuzzy K-means clustering. Four PCs with eigen values more than 1 dominated 84.44% of the total variance, so they were retained for clustering analysis. Three MZs were delineated based on the two criteria modified partition entropy (MPE) and fuzzy performance index (FPI). The studied soil properties differed significantly among MZs. Thus, the methodology used for MZ delineation could be used effectively for soil site-specific nutrient management for avoiding soil degradation concurrently with maximizing crop production in the study area.

Delineation of management zones with spatial data fusion and belief theory

Precision agriculture, as part of modern agriculture, thrives on an enormously growing amount of information and data for processing and application. The spatial data used for yield forecasting or the delimitation of management zones are very diverse, often of different quality and in different units to each other. For various reasons, approaches to combining geodata are complex, but necessary if all relevant information is to be taken into account. Data fusion with belief structures offers the possibility to link geodata with expert knowledge, to include experiences and beliefs in the process and to maintain the comprehensibility of the framework in contrast to other “black box” models. This study shows the possibility of dividing agricultural land into management zones by combining soil information, relief structures and multi-temporal satellite data using the transferable belief model. It is able to bring in the knowledge and experience of farmers with their fields and can thus offer practical assistance in management measures without taking decisions out of hand. At the same time, the method provides a solution to combine all the valuable spatial data that correlate with crop vitality and yield. For the development of the method, eleven data sets in each possible combination and different model parameters were fused. The most relevant results for the practice and the comprehensibility of the model are presented in this study. The aim of the method is a zoned field map with three classes: “low yield”, “medium yield” and “high yield”. It is shown that not all data are equally relevant for the modelling of yield classes and that the phenology of the plant is of particular importance for the selection of satellite images. The results were validated with yield data and show promising potential for use in precision agriculture.

Delineation of site-specific management zones for pest control purposes: Exploring precision agriculture and species distribution modeling approaches

Delineation of management zones is one key feature of precision agriculture (PA), an agricultural management strategy which aims to maximize productivity and sustainability of crops by making optimum use of inputs. Common means of achieving this include the delineation of site-specific management zones (SSMZ), which are subregions of crop fields where yield-limiting factors manifest homogeneously. For pest management, however, PAs standard approach to crop field zoning (e.g. mapping zoning factors, mapping responses, filtering zoning factors, field partitioning, validation of SSMZ) is difficult to implement. In this case, high dynamism of pest populations makes mapping zoning factors and response variables complex and resource-consuming endeavors. Moreover, partitioning crop fields for pest management purposes requires consideration of temporally dynamic zoning factors instead of stable ones. In this paper we propose the addition of an ecological layer to classic PA zoning approaches, in order to overcome obstacles to delineating SSMZ for pest management purposes. In particular, we explore the potential of precision sampling tools (unmanned aerial vehicles, Arduino data loggers) and species distribution modeling (SDM) as facilitators of such an ecological dimension. After contrasting two PA zoning methods against one novel SDM-based approach (described here) to partition a commercial orchard (9 ha, Citrus latifolia Tanaka) in terms of six virtual pests, results show several advantages in our SDM-based method and the occasional necessity of multi approach methodologies to assure adequate zone delimitation under particular infestation scenarios.

Delineation of management zones in agricultural fields using cover–crop biomass estimates from PlanetScope data

Several methods have been proposed to delineate management zones in agricultural fields, which can guide interventions of the farmers to increase crop yield. In this study, we propose a new approach using remote sensing data to delineate management zones at three farm sites located in southern Brazil. The approach is based on the hypothesis that the measured aboveground biomass (AGB) of the cover crops is correlated with the measured cash-crop yield and can be estimated from surface reflectance and/or vegetation indices (VIs). Therefore, we used seven different statistical models to estimate AGB of three cover crops (forage turnip, white oats, and rye) in the season prior to cash-crop planting. Surface reflectance and VIs were used as predictors to test the performance of the models. They were obtained from high spatial and temporal resolution data of the PlanetScope (PS) constellation of satellites. From the time series of 30 images acquired in 2017, we used the PS data that matched the dates of the field campaigns to build the models. The results showed that the satellite AGB estimates of the cover crops at the date of maximum VI response at the beginning of the flowering stage were useful to delineate the management zones. The cover-crop AGB models that presented the highest coefficient of determination (R2) and the lowest root mean square (RMSE) in the validation and test datasets were Support Vector Machine (SVM), Cubist (CUB) and Stochastic Gradient Boosting (SGB). For most models and cover crops, the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) were the two most important AGB predictors. At the date of maximum VI at the beginning of the flowering stage, the correlation coefficients (r) between the cover-crop AGB and the cash-crop yield (soybean and maize) ranged from +0.70 for forage turnip to +0.78 for rye. The fuzzy unsupervised classification of the cover-crop AGB estimates delineated two management zones, which were spatially consistent with those obtained from cash-crop yield. The comparison between both maps produced overall accuracies that ranged from 61.20% to 68.25% with zone 2 having higher cover-crop AGB and cash-crop yield than zone 1 over the three sites. We conclude that satellite AGB estimates of cover crops can be used as a proxy for generating management zone maps in agricultural fields. These maps can be further refined in the field with any other type of method and data, whenever necessary.

Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning

Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324–333, 2015) was developed and tested for estimating sub-field maize (Zea mays L.) yields at 10–30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.

Micro-Spatial Analysis of Maize Yield Gap Variability and Production Factors on Smallholder Farms

Site-specific land management practice taking into account variability in maize yield gaps (the difference between yields in the 90th percentiles and other yields on smallholder farmers’ fields) could improve resource use efficiency and enhance yields. However, the applicability of the practice is constrained by inability to identify patterns of resource utilization to target application of resources to more responsive fields. The study focus was to map yield gaps on smallholder fields based on identified spatial arrangements differentiated by distance from the smallholder homestead and understand field-specific utilization of production factors. This was aimed at understanding field variability based on yield gap mapping patterns in order to enhance resource use efficiency on smallholder farms. The study was done in two villages, Mukuyu and Shikomoli, with high and low agroecology regarding soil fertility in Western Kenya. Identification of spatial arrangements at 40 m, 80 m, 150 m and 300 m distance from the homestead on smallholder farms for 70 households was done. The spatial arrangements were then classified into near house, mid farm and far farm basing on distance from the homestead. For each spatial arrangement, Landsat sensors acquired via satellite imagery were processed to generate yield gap maps. The focal statistics analysis method using the neighborhoods function was then applied to generate yield gap maps at the different spatial arrangements identified above. Socio-economic, management and biophysical factors were determined, and maize yields estimated at each spatial arrangement. Heterogeneous patterns of high, average and low yield gaps were found in spatial arrangements at the 40 m and 80 m distances. Nearly homogenous patterns tending towards median yield gap values were found in spatial arrangements that were located at the 150 m and 300 m. These patterns correspondingly depicted field-specific utilization of management and socio-economic factors. Field level management practices and socio-economic factors such as application of inorganic fertilizer, high frequency of weed control, early land preparation, high proportion of hired and family labor use and allocation of large land sizes were utilized in spatial arrangements at 150 and 300 m distances. High proportions of organic fertilizer and family labor use were utilized in spatial arrangements at 40 and 80 m distances. The findings thus show that smallholder farmers preferentially manage the application of socio-economic and management factors in spatial arrangements further from the homestead compared to fields closer to the homestead which could be exacerbating maize yield gaps. Delineating management zones based on yield gap patterns at the different spatial arrangements on smallholder farms could contribute to site-specific land management and enhance yields. Investigating the value smallholder farmers attach to each spatial arrangement is further needed to enhance the spatial understanding of yield gap variation on smallholder farms.

Delineation of management zones and optimization of irrigation scheduling to improve irrigation water productivity and revenue in a farmland of Northwest China

Precision agriculture has been increasingly practised in recent years. Under precision management, farmland is divided into several management zones to implement different strategies and improve irrigation water productivity and revenues. In this research, six soil properties (silt, sand, soil moisture content, available nitrogen, electrical conductivity and elevation) were selected, and fuzzy c-means clustering was used to delineate management zones. The field was divided into three zones. The differences in the mean of the soil properties among the zones were large and within a zone were small. The coefficient of variation of the properties and yield were also smaller than that before classification. The optimization was carried out by using a genetic algorithm based on the Jensen model. Three objective functions were set as maximum yield, maximum irrigation water productivity (IWP) and maximum revenue, and the weights were kept equal to 1/3. The WHCNS (soil water heat carbon nitrogen simulator) model was used to simulate the maize yield under optimized irrigation schedule for the three management zones and to calculate IWP and revenue. Compared with uniform management, IWP and revenue were increased by 0.6 kg m−3 and $61 ha−1, respectively. The optimized irrigation schedule can be used as a reference for the actual irrigation management. It can increase the IWP and revenue under the premise of achieving the target yield. The results show that the method can guide precision agricultural production and management in large-scale farmland.

Spatial management strategies for nitrogen in maize production based on soil and crop data

Nitrogen (N) fertilisation determines maize grain yield (MGY). Precision agriculture (PA) allows matching crop N requirements in both space and time. Two approaches have been suggested for precision N management, i.e. management zones (MZ) delineation and crop remote and proximal sensing (PS). Several studies have demonstrated separately the advantages of these approaches for precision N application. This study evaluated their convenient integration, considering the influence of different PA techniques on MGY, N use efficiency (NUE), and farmer's net return, then providing a practical tool for choosing the fertilisation strategy that best applies in each agro-environment. A multi-site-year experiment was conducted between 2014 and 2016 in Colorado, USA. The trial compared four N management practices: uniform N rate, variable N rate based on MZ (VR-MZ), variable N rate based on PS (VR-PS), and variable N rate based on both PS and MZ (VR-PSMZ), based on their effect on MGY, partial factor productivity (PFPN), and net return above N fertiliser cost (RANC). Maize grain yield and PFPN maximisation conflicted in several situations. Hence, a compromise between obtaining high yield and increasing NUE is needed to enhance the overall sustainability of maize cropping systems. Maximisation of RANC allowed defining the best N fertilisation practice in terms of profitability. The spatial range in MGY is a practical tool for identifying the best N management practice. Uniform N supply was suitable where no spatial pattern was detected. If a high spatial range (>100 m) existed, VR-MZ was the best approach. Conversely, VR-PS performed better when a shorter spatial range (<16 m) was detected, and when maximum variability in crop vigour was observed across the field (range of variation = 0.597) leading to a larger difference in MGY (range of variation = 13.9 Mg ha−1). Results indicated that VR-PSMZ can further improve maize fertilisation for intermediate spatial structures (43 m).

An integrated approach for the rectangular delineation of management zones and the crop planning problems

This article proposes an integration of the Delineation of Rectangular Management Zones (DRMZ) with the Crop Planning Problem (CPP) in precision agriculture. The first problem consists in partitioning the agricultural fields into chemical and physical management zones satisfying a specific homogeneity level considering the soil properties. These partitions are commonly used to improve agricultural practices such as fertilization, irrigation and pests control, to name a few. The second problem considers the management zones, to determine the best crop for each plot maximizing the profit. We propose an integrated optimization problem determining the decisions of both problems simultaneously. The latter problem is formulated through a non-linear program, and we implement linearization techniques to obtain high-quality solutions with a commercial solver for instances generated at random based on real-data of agricultural activities in Mexico. We compare our integrated method to a sequential approach based on solving the DRMZ that minimizes the number of management zones, and then, finding a solution for the CPP. Numerical results for the integrated method restricted to the number of management zones found by the sequential one show an average increment of 1.77% in the profit compared to the sequential approach, but the average increment can be up to 5.38% when there are no limitations for the number of management zones. Finally, a sensitivity analysis considering variability for the amount of water shows that it may be recommended to define a non-deterministic approach in further research due to the significant changes in the obtained profits.

Modelling DUALEM-2 measured soil conductivity as a function of measuring depth to correlate with soil moisture content and potato tuber yield

Electromagnetic induction (EMI) is a non-destructive technique to measure the apparent electrical conductivity (ECa) of agricultural soils. It has become popular in precision agriculture applications to estimate crop yield from ECa. This study was conducted to relate ECa with soil moisture content (θv) and potato tuber yield under Prince Edward Island (PEI), Canada growing conditions and to establish a two-layered soil model for ECa. During year one, horizontal co-planar (HCP), perpendicular co-planar (PRP), and θv were measured with DUALEM-2 sensor and time domain reflectometry (TDR) probe and correlated with yield measured from 12 m2 grids at 40 locations in each of the two potato fields on PEI. During 2018, vertical electrical sounding was undertaken by raising the DUALEM-2 from ground-level at 0.2 m intervals to a height of 1.6 m to investigate variations in ECa with soil depth. A two-layered soil model was developed using generalized reduction gradients to determine the surficial conductivity and thickness, and subsurface conductivity. ECa variables (HCP and PRP), θv, and yield were significantly correlated and remained consistent throughout the study period, indicating their potential in predicting potato tuber yield. ECa in the fields varied both spatially and laterally with depth. GIS interpolated maps of a representative set of data for HCP and tuber yield (2017 data) showed structured spatial patterns and thus feasibility for delineating management zones. Further studies should examine the potato root zone (0.15–0.3 m) ECa specifically for developing higher layered models, which may be useful for accurate prediction of tuber yield. Non-destructive and accurate yield mapping using DUALEM-2 sensor combined with mathematical modelling of soil ECa layering has potential for use in developing management zones in potato fields.

SITE-SPECIFIC RECLAMATION MAPS OF A SALT AFFECTED SOIL IN ISMAILIA GOVERNORATE

Delineation of soil management zones is essential for precision agriculture applications, to know agricultural inputs added to soil. Geostatistics provides an effective tool for soil spatial variability and delineating management zones. Spatial variability was done on soil properties to prepare prescription maps for leaching and gypsum requirements (GR) of a salt affected soil. The performance of the spatial model was evaluated by calculating two different statistics. Mean error (ME) as a measure of precision and mean square standardized error (MSSE) as a measure of accuracy were calculated. The developed spatial maps of investigated soil parameters [electrical conductivity (ECe), exchangeable sodium percentage (ESP) and cation exchange capacity (CEC)] were used to specify 4 zones-prescription maps that need leaching and gypsum requirements. Results showed that the three models were precise and accurate with ME and MSSE values (-0.110, -0.210, 0.002 and 0.937, 1.033, 1.010), respectively. Applying site-specific management for leaching proved cost-effective and beneficial effects compared with traditional management which is based on an average value over the experimental field. The (GR) on basis of traditional management was cost-effective but was not more beneficial because zones 1 and 2 showed GR more than needed and vice versa for zones 3 and 4. The high cost in this case could be counterbalanced by the expected increased production in zones with high ESP.

The effects of denitrification parameterization and potential benefits of spatially targeted regulation for the reduction of N-discharges from agriculture

Diffuse nitrate leaching from agricultural areas is a major environmental problem in many parts of the world. Understanding where in a catchment nitrate is removed is key for designing effective land use management strategies that protect water quality, while minimizing the impact on economic development. In this study we assess the effects of spatially targeted nitrate leaching regulation in a basin with limited knowledge of the complexity of chemical heterogeneity. Three alternative nitrate reactivity spatial parameterizations were incorporated in a catchment-scale flow and transport model and used to evaluate the effectiveness of four possible spatially targeted regulation options. Our findings confirm that denitrification parameterization cannot be numerically determined based on model inversion alone. Detailed field based characterization using physical and geochemical methods should be considered and incorporated in the numerical inversion scheme. We also demonstrate that there are potential benefits of implementing spatially targeted regulation compared to spatially uniform regulation. Focusing regulation in areas where nitrate residence time is short, such as riparian zones or areas with low natural N-reduction, results in greater reduction of N-discharges through groundwater. Significantly improved efficiencies can be expected when delineation of management zones considers the chemical heterogeneity and groundwater flow paths. These improved efficiencies are achieved by adopting management rules that regulate land use in discharge sensitive areas, where leaching changes contribute the most to the catchment nitrate discharges. In our case study, regulation in discharge sensitive zones was twice as efficient compared to other management options.