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Abstract

Efficient and cost-effective methods are needed for delineating sub-field productivity zones to improve soil and crop site-specific management. This investigation was conducted to answer the question of whether apparent soil electrical conductivity (EC(a)) and elevation could be used to delineate productivity zones (SPZ) for claypan soil fields that would agree with productivity zones delineated from yield map data (YPZ). Ten and seven years of combine-monitored yield maps were available for two Missouri claypan soil fields, designated Field 1 and Field 2, respectively. The fields were generally cropped in corn and soybean. Soil EC(a) data were collected with a non-contact, electromagnetic induction-based EC(a) sensor (Geonics EM38) and a coulter-based sensor (Veris model 3100). Elevation data were collected using a real-time kinematic GPS. Unsupervised fuzzy c-means clustering was independently used both on yield data to delineate three YPZ and on combinations of EC(a) and/or elevation data to delineate three SPZ. Outcomes of YPZ and SPZ were matched and agreement calculated with an overall accuracy statistic and a statistical index called the Kappa coefficient. Best performing combinations of EC(a) and elevation variables gave 60-70% agreement between YPZ and SPZ. We consider this level of agreement promising, especially considering that there were many other yield-limiting factors unrelated to EC(a) and elevation. Generally, multiple variables of EC(a) and elevation were better than a single variable for generating SPZ. The specific combinations of EC(a) and/or elevation variables that gave highest agreement between YPZ and SPZ were field specific. Based on these findings, we conclude EC(a) and elevation measurements can be reliably used for creating productivity zones on claypan soil fields.

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... • Kitchen et al. (2005) compared the productivity zones (SPZ) delineated using ECa and elevation with the ones from yield map data (YPZ). ...
... Usually, on MZs delineation, the yield is used as target values. Kitchen et al. (2005) researched two Missouri claypan soil fields to answer the question of whether ECa and elevation could be used to delineate productivity zones (SPZ) that would agree with productivity zones delineated from yield map data (YPZ). Source: Kitchen et al. (2005). ...
... Kitchen et al. (2005) researched two Missouri claypan soil fields to answer the question of whether ECa and elevation could be used to delineate productivity zones (SPZ) that would agree with productivity zones delineated from yield map data (YPZ). Source: Kitchen et al. (2005). ...
... There is no single universally accepted delineation method (Guastaferro et al. 2010). Many delineation methods in use first normalize and average or otherwise combine all the years' data before performing the delineation (Georgi et al. 2017;Kitchen et al. 2005;Diker et al. 2003;Kitchen et al. 2003). This normalization and average are both lossy operations (Diker et al. 2003). ...
... There are some different ideas of what exactly constitutes the "management zones" into which a field should be divided. This work used the "yield zone" idea of what a management zone is, sometimes also called "response zones" or yield "productivity zones" (YPZ) (Kitchen et al. 2005;Diker et al. 2003;Kitchen et al. 2003). Therefore, this work and the described model take this YPZ view of what management zones are. ...
... Estimating the means and variances for each year allows the model to handle year-toyear variability in the crops, or even different crops being planted in different years. This removes the need for the yield normalization that is typically done when dealing with multiple years (Diker et al. 2003;Kitchen et al. 2005;Georgi et al. 2017), which can result in loss of information (Diker et al. 2003). ...
Article
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Modern precision agriculture equipment enables site-specific management by allowing different treatments for different parts of a field. This ability to subdivide the field calls for identifying management zones. A compromise between treating a field uniformly and treating every plant individually is needed, as the former does not maximize yields and the latter is often impractical. This work presents an algorithm for inferring the yield productivity zones (YPZ) for a field based on yield data from multiple years. The algorithm uses a hidden Markov random field model (HMRF) to find regions of the field which likely correspond to the same underlying yield distribution (i.e., productivity zones). These regions are modeled to be the same every year, but their distributions (i.e., yield characteristics) are allowed to vary with time to account for year-to-year variability (from e.g., weather effects, differing crops or crop varieties). The zone assignments and distributions are estimated using stochastic expectation maximization (SEM) and the maximizer of the posterior marginals (MPM). The underlying assumption of the model and algorithm is that the yields corresponding to a given YPZ will behave similarly and therefore derive from the same probability distribution. YPZs are useful inputs for determining management zones. An advantage of this method is that it is able to run with only the yield data which are automatically collected during harvest. Also, this method requires no crop specific calibration or configuration or normalization of the data by year.
... As a biennial crop, wild blueberry is susceptible to variability due to variations in seasons, growing conditions, and management history. Studies have found that soil apparent electrical conductivity (EC a ) relates to several yield-determining properties such as soil organic matter, moisture content, and soil texture [6][7][8][9]. In unsaturated, non-saline soils, EC a has been found to reflect variations in both moisture availability and soil texture [8]. ...
... Studies have found that soil apparent electrical conductivity (EC a ) relates to several yield-determining properties such as soil organic matter, moisture content, and soil texture [6][7][8][9]. In unsaturated, non-saline soils, EC a has been found to reflect variations in both moisture availability and soil texture [8]. EC a may be measured via time domain reflectometry (TDR), electromagnetic induction (EMI), or electrical resistivity. ...
Article
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Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios.
... Soil-based measurement can be used to provide more temporally stable zones. For example, ECa has been used extensively for MZ delineation [39,53] along with elevation mapping using real-time kinematic-GPS [60,98,113,115]. Soil ECa is suitable for explaining spatial variability for static soil properties, however, it does not explain the spatial variability of yield or quality which strengthens the argument of using the combination of different parameters in the process of MZ delineation for meaningful results. ...
... Degree of Agreement References [39,82] delineate soil productivity zone (SPZ) and yield productivity zone (YPZ) using unsupervised fuzzy k-means clustering. They calculated degree of agreement by matching outcomes of YPZs and SPZs with overall accuracy statistics (matched cells divided by total cells in data sets). ...
Chapter
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In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed.
... The ECa has been successfully used in agricultural crops (Kitchen et al. 2005;Singh et al. 2016) and it has the potential to be used as a pasture yield estimator because some of the soil properties that are related to ECa have a high spatial heterogeneity in soils used for livestock production (McCormick et al. 2009;Cicore et al. 2015;Peralta et al. 2015a). However, it is important to take into account the fact that some important soil properties affecting forage yield, such as nitrogen (N) availability, are not related to the ECa (Kuang et al. 2012;). ...
... This positive water balance may have evened the potential production of biomass-sampling areas located in different ECa zones because water availability is the main limiting growth factor in temperate regions (Fraisse et al. 2001). In this sense, Kitchen et al. (2005) found that the correlation results between ECa measurements and yield were influenced by precipitation during the growth period of summer crops. Because of the low association between AB an ECa during the autumn growing season (Fig. 5b), the RMSE and RRSME were not calculated. ...
Article
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Mapping of the apparent soil electrical conductivity (ECa) can be used to estimate the variability of forage yield within a plot. However, forage production can vary according to the growing season and to soil properties that do not affect the ECa (e.g. nitrogen (N) content). The aim of this study was to assess the relationship between ECa and forage yield of tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) during different regrowth periods and contrasting levels of N availability and then use this information to determine potential management zones. The ECa was measured and geo-referenced in a 5.75-ha paddock that sustained a permanent pasture dominated by tall fescue. In addition, a 30m by 30m grid cell size was chosen and 43 sampling areas, each 4m2 in size, were geo-referenced and divided into two experimental units of 1mby 2 m, one of which was fertilised with 250 kgN ha–1 (N250) at the beginning of four regrowth periods (spring 2015, spring 2016, autumn 2016 and autumn 2017) and the other was not fertilised with N (N0). At the end of each regrowth period, we estimated the accumulated biomass. During the spring growing season, accumulated biomass was positively associated with ECa in both N0 and N250 treatments (R2 = 47% and 54%, respectively). By contrast, in autumn, accumulated biomass and ECa were poorly associated (R2 = 10% and 27% for N0 and N250). This may be due to seasonal interactions that alter soil–yield relationships.To assess whether ECa can be used to determine management zones, the differences in accumulated biomass were compared through analysis of variance. Results showed that ECa is associated with the spatial distribution of tall fescue forage yield variability in spring at different N availabilities. Thus, ECa can be reliably used for defining management zones in marginal soils under permanent pastures.
... Such imaging technique needs to be combined with soil-core samples to gain insight on the delivered signal and to map various parameters: soluble salts, clay content and mineralogy, soil water content or organic matter [2]. This description of soil variability at the intra-field scale appears useful for effective management of agricultural fields, delineating areas with similar characteristics that serve as a basis for the modulation of cultural practices [3]. Nevertheless, the cost of such a technology may limit its implementation, and complementary or even substitution methods must be identified. ...
... This potential for assessing soil variability from remote sensing data seems particularly promising, particularly in the context of precision agriculture. Indeed, the modulation of cultural practices (e.g., seedling density, nitrogen intake) is a corollary of the identification of intra-plot patterns that make it possible to partition zones with supposedly similar properties [3]. Nevertheless, without ancillary data, the deeper interpretation of the area remains limited, as for electrical resistivity measurements where soil-core samples need to be collected to analyze the delivered signal [2]. ...
Conference Paper
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The aim of this study is to assess the possibilities of the VNIR (Visible and Near InfraRed) and SWIR (Short Wavelength InfraRed) satellite data for estimating intra-plot patterns of soil electrical resistivity consistent with ground measurements. The methodology is based on optical reflectances that constitute the input variables of random forest, alone or in combination with parameters derived from a digital elevation model (DEM). Over a field located in southwestern France, the results show high level of accuracy for the 0–50 and 0–100 cm soil layers (with R² of 0.69 and 0.59, and a relative RMSE of 18% and 16%, respectively), the performances being lower for the 0–170 cm layer (R² of 0.39, relative RMSE of 20%). The combined use of optical reflectances with parameters derived from the DEM slightly improves the performances, whatever the considered layer. The influence of each reflectance on soil electrical resistivity estimates is finally analyzed, showing that the wavelengths acquired in the SWIR have a relative higher importance than VNIR reflectance.
... It can be observed in Fig. 2 that the use of more than one layer of information in the definition of management zones can be interesting, if this information contains characteristics of interest, such as spatial continuity and similarity with the 6 It can be observed in Fig. 2 that the use of more than one layer of information in the definition of management zones can be interesting, if this information contains characteristics of interest, such as spatial continuity and similarity with the spatial pattern of the attributes of interest, for soil fertility correction. The authors [26] and [21] indicate that the use of two information for delimitation of management zones provides better results. ...
... attern of the attributes of interest, for soil fertility correction. The authors [26] and [21] indicate that the use of two information for delimitation of management zones provides ...
... Las fuentes de información utilizadas para el estudio de la variabilidad y la delimitación de zonas de manejo incluyen propiedades del suelo (Kitchen et al., 2005), imágenes satelitales (Schepers, 2001) y mapas de rendimiento (Kleinjan et al., 1999). Las zonas de manejo (ZM) se definen como subregiones dentro de los campos donde se expresa una combinación homogénea de factores determinantes del rendimiento y para las cuales es apropiada una determinada combinación de insumos (Doerge, 1999). ...
... La variabilidad temporal puede evaluarse usando mapas multianuales (Kitchen et al., 2005), pero para ello es necesario la normalización de los datos lo que no permite identificar a los productores oportunidades económicas. Transformar los datos de rendimiento a alguna variable económica permite combinar varios años de datos de diferentes cultivos, evaluando el sistema de cultivo como un todo (Massey et al., 2008). ...
Conference Paper
Grain yield (RNM) spatio-temporal variability have been considered in management zones (ZM) delimitation, however crops gross margin (MBM) has been less taken into account. In regions where the instability of agriculture production and the interanual variability of grain and inputs prices highly affects crops economic profitability, compare the mentioned approaches could be an important contribution. The objectives were to: i) delimitate ZM by using management zone analyst (MZA) based on multi-years RNM maps, ii) delimitate ZM considering spatio-temporal varibility of RNM and MBM, iii) compare ZM concordance between zone delimitation methods and using RNM or MBM maps. The RNM maps were get from three fields (L1, L2 and L3) of a farm located in Diamante, Entre Ríos, Argentina (-32°12´44´´, -60°32´42´´). Grain yield maps from several crops and years adequately conditioned, were 10x10m gridded to calculate average normalized RNM and variability (CV, variation coefficient). The MBM was calculated as the product of RNM and price minus production costs. The RNM maps were used to delimitate zones using MZA. Also, the RNM and MBM maps were used to delimitate 4 zones in each field: ZM1(RNM or MBM), high and stable RNM or MBM; ZM2(RNM or MBM), low and stable RNM or MBM; ZM3(RNM or MBM), high and unstable RNM or MBM; and ZM4(RNM or MBM), low and unstable RNM or MBM. Data sets were splitted in low or high RNM or MBM, according they were below or above RNM or MBM average. Stable and unstable zones were delimitated considering the CV corresponding to the 75% of data of the accumulated distribution of RNMCV or MBMCV for each field. Comparison of delimitation methods was performed using the land use change tools included in the Semiautomatic Classification plugging of QGIS. In L1 and L3, MZA software delimited two ZM, whereas in L2 delimited ZM were three. Thresholds to classify stable and unstable data, ranged from 37% to 46% for RNM, and from 88% to 110% for MBM. Zones delimited in L1 using MBM and RNM differed only from 7% to 4% from ZM delimited using MZA. Remarkable results were obtained comparing ZM delimited using RNMCV with those using MBMCV. The differences between RNMCV and MBMCV was 30%, where main change was from L1ZM2(RNM), to L1ZM4(MBM), (12%) and L1ZM3(RNM) to L1ZM1(MBM). In L2, data of RNMCV and MBMCV classification changed 44-46 %, respect to MZA. The main difference was observed in MBMCV, where 10% from medium RNM from MZA were classified as L2ZM4(MBM). In L2, delimitated ZM using MBMCV changed 21% respect to RNMCV. Field that showed less changes among classification methods was L3. Zone delimited with RNMCV respect to MZA changed only 3%, whereas MBMCV respect to MZA changed 14%. Comparing zoning using MBMCV respect to RNMCV showed changes of 22% distributed in similar proportions among classes. Zoning using MBM variability may be a more useful method than using RNM to adjust management practices.
... Many researches have used soil ECa to define management zones in the temperate region (e.g. Johnson et al. 2001;Ferguson, Lark, and Slater 2003;Johnson et al. 2003;Kitchen et al. 2005;Li et al. 2008) however, there has been limited research in the humid tropics (Aimrun et al. 2011;Hudzari et al. 2013). Although GIS has been used in a site specific management (SSM) context in cocoa fields, (Espinosa et al. 2006), there are no reports of SSM of cocoa plantations using soil ECa to assess the spatio-temporal stability of key soil properties. ...
Thesis
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Exports of cocoa (Theobroma cacao L.) from Trinidad and Tobago have been steadily declining. Soil spatial variability has been implicated as one of the major causes of inconsistency in Trinidad and Tobago's cocoa yields. Field-scale characterization of soil spatiotemporal variability for the implementation of site-specific management (SSM) has the potential to optimize efficiency, profitability, sustainability, and reduce environmental risks in Trinidad and Tobago's large cocoa plantations. Traditional methods such as grid sampling combined with geostatistical analysis are labour intensive, time-consuming, invasive, and costly. Electromagnetic induction (EMI) devices, allow rapid field-scale characterization of soil apparent electrical conductivity (ECa). The EMI device has, however, had limited usage under tropical conditions. In this study, the potential of the EMI geophysical technique for determining spatial and temporal variations of soil properties and identifying soil-based management zones for site-specific management in cocoa fields was evaluated. 4 Spatial measurements of ECa at shallow, ECas (0-0.75 m) and deep, ECad (0.75-1.5 m) were conducted using EMI at the International Cocoa Genebank, Trinidad (ICGT). Results of correlation analysis show that ECad and ECas gave the strongest linear correlation with clay-silt content (r = 0.67 and r = 0.78, respectively) and soil solution electrical conductivity, ECe (r = 0.76 and r = 0.60, respectively). Spearman's rank correlation coefficients (rs) ranged between 0.89 and 0.97 for ECad and 0.81 and 0.95 for ECas signifying a strong linear dependence between measurement days. Thus, in the humid tropics, cocoa fields with thick organic litter layers and relatively dense understory cover, experience minimal fluctuations in transient properties of soil water and temperature at the topsoil resulting in similarly stable ECas and ECad. Multiple linear regressions indicated that clay-silt content and ECe dominated the signal surface response at both ECad and ECas depths accounting for 67% and 63% in ECa variability, respectively. Since ECas covers the depth where cocoa feeder roots concentrate and is similarly temporally stable as ECad, ECas of the wettest month surveyed (August 2009) was used as a secondary data in cokriging to improve the spatial and temporal estimation of clay-silt content and ECe. The cokriged data was subjected to fuzzy cluster classification to delineate management zones. Two management zones were identified using the fuzzy performance index and normalized classification entropy. This zone delineation potentially facilitates cost-effective, environmentally friendly, and energy-efficient management of the field. Future work might want to relate the proposed management zones with yield maps, to demonstrate the agronomic benefits of this classification.
... According to [69], delineation of management zones is an effective way to manage the variability of soil within a field, such that each zone will receive specific management. In [145], a management zone is defined as a subregion of a field that has a relatively homogeneous combination of yield-limiting factors, for which a single rate of a specific crop input is appropriate to reach maximum efficiency of farm inputs. ...
Article
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Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.
... Precision agriculture is important in enhancing crop productivity and increasing nutrient use efficiency [1]. Numerous studies have applied site-specific nutrient management to large-scale farmland [2,3]. However, few investigations have been conducted on medium-and small-scale farms, especially in densely populated countries such as China and India. ...
Article
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Soil is characterized by high spatiotemporal variability due to the combined influence of internal and external factors. The most efficient approach for addressing spatial variability is the use of management zones (MZs). Common approaches for delineating MZs include K-means and fuzzy C-means cluster analysis algorithms. However, these clustering methods have been used to delineate MZs independent of the spatial dependence of soil variables. Thus, the accuracy of the clustering results has been limited. In this study, six soil variables (soil pH, total nitrogen, organic matter, available phosphorus, available potassium, and soil apparent electrical conductivity) were used to characterize the spatial variability within a representative village in Suining County, Jiangsu Province, China. Two variable reduction techniques (PCA, multivariate spatial analysis based on Moran’s index; MULTISPATI-PCA) and three different clustering algorithms (fuzzy C-means clustering, iterative self-organizing data analysis techniques algorithm, and Gaussian mixture model; GMM) were used to optimize the MZ delineation. Different clustering model composites were evaluated using yield data collected after the wheat harvest in 2020. The results indicated that the variable reduction technologies in conjunction with clustering algorithms provided better performance in MZ delineation, with average silhouette coefficient (ASC) and variance reduction (VR) of 0.48–0.57, and 13.35–23.13%, respectively. Moreover, the MULTISPATI-PCA approach was more conducive to identifying variables requiring MZ delineation than traditional PCA methods. Combining MULTISPATI-PCA and the GMM algorithm yielded the greatest VR and ASC values in this study. These results can guide the optimization of MZ delineation in intensive agricultural systems, thus enabling more precise nutrient management.
... The goal of precision viticulture is the delineation of management zones inside the vineyard that have the same soil characteristics (soil texture, topography, electrical conductivity, etc.) and the same crop characteristics (vigor, yield characteristics, quality characteristics, etc.). In this way, the advantages of the vineyard's variability in favor of the producer are fully exploited [3]. ...
Article
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Precision viticulture employs various sensors for assessing nondestructively key parameters in vineyards. One of the most promising technologies for this purpose is the laser scanner sensor. Laser scanner uses the LiDAR (Light Detection And Ranging) method for the calculation of the distance from the sensor. However, the number of cultivation operations affects the credibility of sensors such as the laser scanner. The main aim of this study was to assess a laser scanner sensor at different measurement settings for estimating pruning wood parameters on two wine grape cultivars (Sauvignon Blanc and Syrah) that received different numbers of farming interventions. The experiment was conducted in the two vineyards situated in the same farm for two successive years (2014 and 2015). The results indicated that the use of a laser scanner in the Syrah vineyard presented more accurate results (r = 0.966 in 2014 and r = 0.806 in 2015) when compared to the Sauvignon Blanc one (r = 0.839 in 2014 and r = 0.607 in 2015) regarding pruning wood parameters estimation. Different measurement settings and weather conditions had different effects on the accuracy of the sensor. It can be concluded that the laser scanner is a very helpful sensor for estimating pruning wood parameters in vineyards.
... The Management Zone Analyst software (MZA 1.0.1, University of Missouri-Columbia, Columbia, Missouri, USA) [25] was used to delineate the number of the table-grapes vineyard management zones based on the soil electrical conductivity [26]. The MZA uses fuzzy c-means unsupervised clustering algorithms for conducting zones' clustering [25]. ...
Article
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Climatic conditions have been acknowledged to affect table-grapes production and postharvest quality. The correlation of the monitored environmental conditions such as rainfall and ambient temperature, with postharvest quality properties such as pedicel detachment force, pH, brix, grape volume, for three cultivation years 2015-2017 was analysed. The significance of the climatic variability on the postharvest quality was examined based on a hydrothermic index used in viticultural zoning namely Branas, Bernon, Levadoux (BBL) for two phenological stages the Budbreak-Flowering and Flowering-Veraison. Their analysis revealed correlations of good efficacy between the BudBreak-Flowering BBL and Flowering-Veraison BBL with the postharvest quality, adopting the Box-Cox transformation for variance stabilization and accuracy improvement of Pearson correlation between the analysed variables. The statistical analysis also revealed specific dependencies of the tested quality properties from the climatic indices and management zones that could be implemented in the postharvest management of table-grapes, according to the management zones delineation as these are determined in the context of the precision viticulture.
... When the assumptions of an ANOVA cannot be assured, the Kruskal-Wallis test and the Dunn test as a post-hoc analysis can be performed [19]. The use of a simple statistical index, such as the Kappa coefficient [20], to indicate the similarity between the maps generated with a reference map can be a suitable tool [21]. However, in the present study, a global index was defined to facilitate the validation process. ...
Article
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The intensification of the Montado mixed ecosystem (agro–silvo–pastoral) is a current endeavor in the context of promoting the sustainability of extensive livestock production in the Mediterranean region. Increased pasture productivity and extensive animal production involves the use of technologies to monitor spatial variability and to implement differentiated management of pasture grazing, fertilization or soil amendment. An intermediate step should lead to the identification and demarcation of areas with similar characteristics (soil and/or crop development), known as homogeneous management zones (HMZ) to implement site-specific management strategies. In this study, soil apparent electrical conductivity (ECa) and altimetry surveys were carried out in six experimental pasture fields with a non-contact electromagnetic induction sensor (EM38) associated with a Global Navigation Satellite System (GNSS) receiver. These ECa and topographic maps were used in geostatistical analyses for designing and establishing final classification maps with three HMZ (less, intermediate and more potential). The normalized difference vegetation index (NDVI), obtained from a proximal optical sensor, and soil and biomass sampling were used to validate these HMZ. From a practical perspective, these HMZ are the basis for preparation of fertilizer prescription maps and use of variable rate technology (VRT) in a Precision Agriculture project.
... EC a is highly correlated with important soil attributes such as clay content and clay minerals (McBratney et al., 2005;Sanches et al., 2019b), soil organic matter (Sanches et al., 2019b;Sudduth et al., 2005), cation exchange capacity Corwin & Lesch, 2005) and soil pH . More interestingly, EC a can also be of value for identifying the yield potential of crops because of the intrinsic relationship between crop yield and such soil matrix properties as soil texture, CEC, SOM and solute content (Godwin et al. 2003;Kitchen et al., 2005;Sanches et al., 2019a). Another positive aspect of this technology is the spatial and temporal stability of EC a readings (Serrano et al., 2017), enabling its use for the site-specific management of crops. ...
Article
Proximal soil sensing Precision farming Site-specific management Soil apparent electrical conductivity (EC a) to survey soil has been demonstrated to be a valuable tool for mapping the soil attributes. Whereas the correlation between EC a and soil attributes is well documented, the practical usage of EC a data to characterise soil properties on individual fields or across many fields is still not recognised in Brazil. Including EC a data from several fields could increase the capacity to characterise soil properties compared to a within-field approach. The objective of this study was to deliver a framework to allow the characterisation of soil spatial variability using EC a data from multiple fields. The ability of EC a to map the content and variability of soil attributes was assessed in six fields, totalling 412 ha and 2000 soil samples. The results indicate a significant correlation between EC a and selected soil properties. EC a data is useful to predict and characterise the content and variability of soil attributes according to EC a classes. Sites with relatively low EC a values presented greater spatial variability of those attributes, which requires more intensive sampling for proper spatial characterization. The amplitude of variation of EC a was directly correlated to clay content (R 2 ¼ 0.95), SOM (R 2 ¼ 0.65) and CEC (R 2 ¼ 0.76) ranges, where 1.0 mS m À1 corresponded to 1.5 g kg À1 , 0.12 g dm À3 and 0.25 mmol c dm À3 , respectively. The spatial variability of those attributes remained stable over different sampling times. The significant correlation of EC a readings to soil attributes indicates a potential framework to manage soil fertility in multiple fields. ScienceDirect jo urnal homepage: www .e lsev ie r.com/ locate/issn/153 75110 b i o s y s t e m s e n g i n e e r i n g 2 1 6 (2 0 2 2) 2 2 9 e2 4 0
... O valor de kappa varia de 1 (completa concordância) até 0 (sem concordância). De acordo com Kitchen et al. (2005), valores altos do índice kappa (próximos de 1) ocorrem quando a concordância espacial entre dois mapas de ZM é máxima. ...
Technical Report
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Resumo – Na fruticultura, onde as áreas de produção são muito pequenas quando comparadas com as culturas de grãos, a representatividade de uma grade de amostras ideal para a aplicação da geoestatística é muito densa com relação ao que é utilizado na agricultura de precisão (AP) tradicional, o que torna a amostragem uma atividade extremamente cara ao produtor. Assim, metodologias que pretendem propor um número mínimo de amostras necessárias para o estabelecimento de atividades voltadas a AP, considerando a representação satisfatória da variabilidade espacial da área e o custo da coleta de dados e análise são importantes para tomadas de decisão com relação ao manejo. Este trabalho teve como objetivo principal estabelecer uma metodologia, baseada em análise de dados, que permita sugerir grades amostrais mínimas necessárias para identificar a variabilidade espacial do solo e da cultura em parreirais de videiras. Os resultados mostram que, mesmo com grades com quantidades reduzidas de quatro amostras por hectares, essa variabilidade espacial já pode ser identificada. Abstract – In orchading, where fields are very small compared to grain crops, the representativeness of an ideal sample grid for the application of geostatistics is very dense compared to that used in traditional precision agriculture (PA), which makes sampling an extremely expensive activity for the farmer. Therefore, methodologies that intend to propose a minimum number of samples necessary for the establishment of activities aimed at PA, considering the satisfactory representation of the spatial variability of the area and the cost of data collection and analysis, are important for decision-making regarding management. The main goal of this work was to establish a methodology, based on data analysis, which allows the suggestion of minimum sampling grids necessary to identify the spatial variability of soil and crop in vineyards. The results show that, even with grids with reduced amounts of four samples per hectare, this spatial variability can already be identified.
... Generally, MZ delineation approaches can be categorized based on the provided data and information from different sources [2]. These methods are generally based on farmers' knowledge [20], soil physical and chemical attributes [21][22][23][24], geomorphology [25], yield [26][27][28][29][30][31], electrical conductivity (EC) [32,33] and RS [17,34,35] data, and also hybrid models that combine information from different data sources [36][37][38][39][40][41][42][43][44][45][46][47]. ...
Article
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Enhancing digital and precision agriculture is currently inevitable to overcome the economic and environmental challenges of the agriculture in the 21st century. The purpose of this study was to generate and compare management zones (MZ) based on the Sentinel-2 satellite data for variable rate application of mineral nitrogen in wheat production, calculated using different remote sensing (RS)-based models under varied soil, yield and crop data availability. Three models were applied, including (1) a modified “RS- and threshold-based clustering”, (2) a “hybrid-based, unsupervised clustering”, in which data from different sources were combined for MZ delineation, and (3) a “RS-based, unsupervised clustering”. Various data processing methods including machine learning were used in the model development. Statistical tests such as the Paired Sample T-test, Kruskal–Wallis H-test and Wilcoxon signed-rank test were applied to evaluate the final delineated MZ maps. Additionally, a procedure for improving models based on information about phenological phases and the occurrence of agricultural drought was implemented. The results showed that information on agronomy and climate enables improving and optimizing MZ delineation. The integration of prior knowledge on new climate conditions (drought) in image selection was tested for effective use of the models. Lack of this information led to the infeasibility of obtaining optimal results. Models that solely rely on remote sensing information are comparatively less expensive than hybrid models. Additionally, remote sensing-based models enable delineating MZ for fertilizer recommendations that are temporally closer to fertilization times.
... These can be defined as subregions in a given area within which seasonal differences in weather, soil, and management are expected to have more or less uniform effects on the crops planted there (Castrignano et al., 2010). Therefore, a specific application of MZ is the mapping of their limits in the field, identifying areas of similar productivity potential, known as "productivity zones" or "yield zones" (Kitchen et al., 2005). These zones facilitate the appropriate management, presenting an important tool for precision agriculture. ...
Article
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The concept of production environments, which is widely used to classify the yield potential of soils, and magnetic susceptibility (MS), is emerging as an important tool for mapping ultra-detailed areas. Given this background, this paper aims to evaluate the use of MS as a tool for the identification of areas with different potential the enhancing of sugarcane yield and quality, and the allocation of varieties. An area of 445 ha was sampled at 1 point every 7 ha, and 14 points were determined for stratified sampling following the top of the landscape. Particle size and MS of samples at depths of 0.0-0.2 and 0.2-0.4 m were analyzed. The data on yield and quality of raw material were obtained from a nine crop season database and biometry performed in the 2018/19 crop season. The multivariate analysis of historical results showed the formation of three groups with different yield and quality potential, with a difference of up to 17.28 mg of cane per hectare between the group with the highest and lowest potential, based on soil MS. An analysis of the performance of the varieties involved showed that MS is effective in identifying areas with different potential for sugarcane yield and quality, differentiating by up to 34.5 % the performance of the same variety in different MS classes and by up to 38.5 % the performance of different varieties in similar MS classes. Thus, MS is an effective tool for identifying areas with different potential for sugarcane yield and quality, and can be used for allocating varieties in the field.
... The wide range of environmental conditions, land use, and suitability differences of agricultural fields make possible a wide diversification of the technical ameliorations. Precision Agriculture (PA) is commonly defined as the process of doing the right action at the right place at the right time; therefore, PA is not just a technology, but rather a management philosophy that is made possible by new technologies [1,2]. Advancements in remote sensing, machinery control systems, crop modelling, weather monitoring, decision making, cloud computing, and big data analysis drive PA to the new revolution in agriculture named smart farming [3]. ...
Article
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Proximal sensing represents a growing avenue for precision fertilization and crop growth monitoring. In the last decade, precision agriculture technology has become affordable in many countries; Global Positioning Systems for automatic guidance instruments and proximal sensors can be used to guide the distribution of nutrients such as nitrogen (N) fertilization using real-time applications. A two-year field experiment (2017–2018) was carried out to quantify maize yield in response to variable rate (VR) N distribution, which was determined with a proximal vigour sensor, as an alternative to a fixed rate (FR) in a cereal-livestock farm located in the Po valley (northern Italy). The amount of N distributed for the FR (140 kg N ha−1) was calculated according to the crop requirement and the regional regulation: ±30% of the FR rate was applied in the VR treatment according to the Vigour S-index calculated on-the-go from the CropSpec sensor. The two treatments of N fertilization did not result in a significant difference in yield in both years. The findings suggest that the application of VR is more economically profitable than the FR application rate, especially under the hypothesis of VR application at a farm scale. The outcome of the experiment suggests that VR is a viable and profitable technique that can be easily applied at the farm level by adopting proximal sensors to detect the actual crop N requirement prior to stem elongation. Besides the economic benefits, the VR approach can be regarded as a sustainable practice that meets the current European Common Agricultural Policy.
... The management units are homogeneous areas that have similar yield-limiting factors (Fridgen et al. 2004;Basso et al. 2007). Soil, topography and fertility information are commonly used by the researchers to define management zones (Kitchen et al. 2005;Ortega and Santib añez 2007). In conventional soil mapping approach, soil management units were delineated based on soil series identifying characteristics (Soil Survey Staff 2017). ...
Article
Classification of fields into management units based on soil variability and fertility is important for spatial crop planning. The present study was conducted in Chukanagallu subwatershed (97 km²), Koppal district of Northern Karnataka Plateau, India to map the soil fertility management units and to analyse the suitability of soil for different crops. Random forest regression and classification algorithms were used to map the differentiating characteristics of soil series (soil depth, coarse fragments and soil colour), physicochemical properties (pH, EC and OC) and fertility parameters (P2O5, K2O, S, Fe, Mn, Zn, Cu, B). Random forest model performed well for the prediction of fertility parameters (R² = 44–73%) and physicochemical properties (R² = 39–83%) compared to soil depth and coarse fragments (R² = 17–18%). Predicted soil fertility parameters and physicochemical properties were used for the delineation of different homogenous fertility management units. Soil series characteristics and fertility parameters were also evaluated using a multi-criteria approach for suitability of soil for cotton, groundnut and rice cultivation and the results showed that major area of subwatershed is moderately suitable for the cultivation of cotton, rice and groundnut. The management units derived from DSM approach were symmetrical in production potential and requires similar management aspects which are useful for appropriate planning of management strategies such as crop selections and nutrient management to achieve sustainable production.
... Areas with stable yields have been used for delineating productivity zones to improve site-specific management of fertilizers or seeds (Basso, Fiorentino, Cammarano, Cafiero, & Dardanelli, 2012;Schepers et al., 2004). Identification of areas of similar productivity potential is of interest to producers who base management decisions on reliable estimates of expected yield (Kitchen, Sudduth, Myers, Drummond, & Hong, 2004). ...
Article
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A time‐series of yield monitor data may be used to identify field areas of consistently low or high yield to serve as productivity zones for site‐specific crop management. However, transient factors that affect yield in 1 yr, but not every year, detract from this approach. The objective of this study was to illustrate Moran eigenvector spatial filtering (MESF) with results from analysis of multi‐year crop yield data from two farm fields in the United States. The MESF method accounts for temporal autocorrelation within a common factor map representing the correlation across years and partitions stochastic geographic variation into spatially structured and unstructured components. Crop rotation data were utilized from a dryland field in east‐central South Dakota and an irrigated field in southwestern Georgia. A random effects (RE) model was estimated that utilized eigenfunctions of a geographic connectivity matrix to account for spatially structured random effects (SSRE) and unstructured random effects (SURE) in standardized z scores of multi‐year crop yield. The MESF method was evaluated with conventional averaging of unfiltered yield data as a reference for comparison. In South Dakota, the SSRE accounted for 26% of the yield variance shared across years. Distinct patterns appeared to be related to changes in soil type and landscape position. The Georgia field yielded similar results. The MESF is effective for revealing structured variation in a time series of yield monitor data and may be useful for defining productivity zones within fields.
... Ahora bien, delimitar ZM es la base para poder realizar un manejo diferencial en cada una de ellas, como así también determinar los factores que producen su variabilidad. Para la delimitación de ZM pueden utilizarse distintas bases de información, herramientas y/o procedimientos, entre éstos están los muestreos de suelos en grilla, mediciones de características topográficas del terreno(Sudduth et al., 1997;Franzen, 1999), mapas de conductividad eléctrica(Kitchen et al., 2005) y mapas de rendimiento de cultivos antecesores(Blackmore 2000;Dobermann et al., 2003;Kemerer, 2003). Entre los mencionados, la información de mapas de rendimiento de sucesivos cultivos anteriores puede ser considerada la mejor opción para delimitar zonas de manejo de productividad diferencial.Sin embargo, éstos no están habitualmente disponibles en muchos lugares. ...
Thesis
La delimitación de zonas de manejo es una práctica habitual para adecuar el manejo de cultivos en Agricultura de Precisión, para lo que se requiere información previa. La disponibilidad de imágenes satelitales permite estimar la productividad de cultivos y realizar la delimitación de zonas. El objetivo de este trabajo fue delimitar zonas de manejo mediante la utilización de un índice de vegetación, considerando alternativamente cultivos de verano e invierno, y validar los resultados con la productividad de un cultivo de soja. Se realizó un análisis del índice de vegetación de diferencia normalizada (NDVI) de 6 campañas para delimitar zonas de manejo utilizando el software Management Zone Analyst. Las zonas se validaron con datos de rendimiento de soja (2017/2018) y adicionalmente se exploraron distintos índices de vegetación durante el ciclo de crecimiento del cultivo. Se discriminaron 3 zonas de manejo. La inclusión de los cultivos de invierno modificó la delimitación, manteniendo un 66% de coincidencia. Todos los índices diferenciaron las zonas delimitadas, y los que utilizan bandas de borde rojo, lo hicieron más tempranamente. El índice de vegetación ajustado al suelo optimizado (OSAVI), y los índices de clorofila no evidenciaron un efecto de saturación. La máxima correlación entre los índices y el rendimiento fue determinada alrededor del estadío R6.
... The combination of soil EC and elevation data generally improved MZ classification (evaluated by multiple-year yield) rather than by using EC alone. However, the optimum number of clusters (MZs) (evaluated by the sum of within-cluster yield) was inconsistent from year to year and was affected by the soil moisture status (Fraisse et al. 2001;Kitchen et al. 2005). The temporal variations of the yield responses of MZs make it difficult to determine consistent financial indicators of precision farming practices. ...
Article
Precision agriculture manages within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. Delineation of field-specific management zones (MZs), representing significantly different yield potentials prescribe the rates of a specific crop inputs within-field. This paper examines multiple-year maize grain yield maps (2014, 2015, 2017 and 2018) and their spatial and temporal variability of within-field datasets (soil electrical conductivity [EC], soil organic matter [OM], and elevation) and climate data. The research was undertaken on a non-irrigated field at New Zealand’s Foundation for Arable Research (FAR) in the Waikato region, to provide a simple, heuristic method to delineate dynamic MZs for crop inputs. Supervised statistical learning models (stepwise multiple linear regression [SMLR], feedforward neural network (FFNN), classification and regression tree (CART), random forest (RF), extreme gradient boosting (XGBoost) and Cubist regression) were implemented to predict spatial yield. Prediction accuracies of the trained models were evaluated by withholding one subset of data for testing. For internal ‘split-sample' validation, CART, random forest and XGBoost produced slightly better statistical predictions (RMSE = 1.9–2.0 and R ² = 0.60–0.63) than Cubist and FFNN (RMSE = 2.1–2.2 and R ² = 0.52–0.57), whereas MLR produced the weakest prediction (RMSE = 2.3 and R ² = 0.51). Spatial yield prediction of individual years, were poor (R ² = 0.07–0.36). Input data used is readily and inexpensive for small arable fields in New Zealand. The methods presented, could be applied to a wider range of arable crops for within-field management inputs, to respond to spatially diverse soil texture distribution and variable rainfall patterns.
... Precision agriculture is a circular process which entails data collection, data analysis, decision-making in management, and evaluation of these decisions [38]. In this way, it allows a reduction of agricultural inputs, obtaining the maximum yield and quality of produced grapes [39]. By precisely measuring variations within a field and adapting the strategy accordingly, winegrowers can significantly increase the effectiveness of pesticides and fertilizers, and use them more selectively [40]. ...
Article
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This study shows a new methodological proposal for wine farm management, as a result of the progressive development of the technological innovations and their adoption. The study was carried out in Italy involving farmers, workers, or owners of wine farms who are progressively introducing or using precision agriculture technologies on their farm. The methodology proposed was divided in four stages (1. understanding the changes in action; 2. identifying the added value of Smart Farming processes; 3. verifying the reliability of new technologies; 4. adjusting production processes) that can be applied at different levels in vine farms to make the adoption of precision agriculture techniques and technologies harmonious and profitable. Data collection was carried out using a participant-observer method in brainstorming sessions, where the authors reflected on the significance of technology adoption means and how to put them in practice, and interviews, questionnaire surveys, diaries, and observations. Moreover, project activities and reports provided auxiliary data. The findings highlighted the issues of a sector which, although with broad investment and finance options, lacks a structure of human, territorial, and organizational resources for the successful adoption of technological innovations. The work represents a basis for the future development of models for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.
... These results are supported by previous research that found soybean net return in no-tillage to be unaffected by DTC across a range of wet and dry years (Conway et al., 2017). This also aligns with a study that found less within-field yield variation in soybean production when compared to corn on claypan soil (Kitchen, Sudduth, Myers, Drummond, & Hong, 2005). These results add more evidence that converting to a no-tillage CS can increase revenue and further decrease variability caused by field areas with reduced DTC. ...
Article
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Abstract Converting from standard tillage or no‐tillage cropping systems to more conservation‐based cropping systems that include no‐tillage, cover crops, and reduced agrichemical inputs must be profitable for large‐scale adoption. Therefore, research was conducted at the central Mississippi River Basin site of the USDA Long‐Term Agroecosystem Research Network from 1996 to 2009 to determine how cropping systems, landscape position, and depth to claypan affected net economic return. Treatments consisted of three cropping systems {mulch‐till corn (Zea mays L.)–soybean [Glycine max (L.) Merr.], MTCS; no‐till corn–soybean, NTCS; no‐till corn–soybean–wheat (Triticum aestivum L.) (NTCSW)–cover crop} and three landscape positions (summit, backslope, and footslope). Within each cropping system, landscape position influenced the depth to claypan and net returns, which were greatest in the summit and footslope positions. Across landscape positions, net return for NTCS was US$252 and $119 ha−1 yr−1 greater than MTCS and NTCSW, respectively. Net return of corn in MTCS and NTCSW was negative, whereas corn in NTCS averaged $97 ha−1 yr−1. Only NTCS corn exhibited a positive linear response in net return to depth to claypan. Soybean was much more profitable than corn, and both NTCS and NTCSW soybean were less influenced by landscape position and had at least $252 ha−1 yr−1 greater return than did MTCS soybean across landscape position. Results suggest that converting from MTCS to NTCS would have large positive impacts on reducing within‐field variability and increasing profitability in the region, and modifications to the NTCSW system are needed to improve profitability.
... MZ are field areas with similar attributes in landscape and soil condition. Zones are considered homogeneous when they have similar electrical conductivity (EC), crop yield, and producer-defined areas (Flowers et al., 2005& Kitchen et al., 2005. Such attributes tend to have similar yield potential, input-use efficiency, and environmental impact from the application of fertilizer. ...
Article
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The efficient use of Nitrogen (N) is one of the essential goals in crop management to achieve a desirable plant production (biomass). N Management is a challenging task and several methods individually or in combination are used to enhance its efficiency. However, only 33 per cent of nitrogen use efficiency (NUE) improved while developing nitrogen management tools and methods. The primary objective to improve nitrogen use efficiency via strategic management such as respective methods, soil testing, plant tissue testing, right ways of fertilizer placement and timing, vegetative indexes (leaf area index) and spectral response etc. No single method was found sufficient to stand the nitrogen loss. Some methods were found time consuming and unsynchronized with N uptake behaviour of crop, for example, plant tissue testing. Use of precision agriculture tools, such as Green Seeker, SPAD meter, and leaf color chart (LCC) were found better as compared to conventional methods such as soil testing, but these tools can only be used when the crop is up. Therefore, N management is possible only through in season N application methods. When 70% of the applied nitrogen is used by the crops within 25-30 days after sowing, for example, corn, it is required to apply major N rates through in season approach and some N at the time of sowing using soil test reports. finally concluded that using two or more methods in combination when managing the N in the crops field.
... As FCM assigns membership degree to each data point w.r.t. each clusters, overlapping clusters can be obtained. In [10] authors have found that, to generate management zones, data from soil Electrical Conductivity (EC) and elevation can be used for obtaining clusters, and they also found from the maps of historical yield data that the accuracy is also comparable. A reference process model is developed by [11] have used the Business Process Model Notation (BPMN) for general description of the steps, flow of the steps and decisions to be made. ...
Article
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Use of Precision Agriculture (PA) is the need of an hour to enhance the crop productivity to meet the increasing demand for food supply. Clustering algorithms have been proven to be the best suitable ones to delineate the management zones (as per soil fertility) in PA. Management zones can be treated as sub-fields, which are homogeneous in soil physical/chemical properties. In this paper, we have proposed a median strange point (MSP) clustering algorithm for the delineation of agricultural management zones. The median strange point algorithm has been compared with the popular clustering algorithms like K-means, Fuzzy C Mean, Possiblistic Fuzzy C Means, and Linde Buzo Gray algorithms. The results obtained demonstrated that for the given number of management zones the median strange point algorithm outputs are at par; in some cases superior to the standard algorithms. The proposed experimentation is carried out on the Sugarcane (Saccharum Officinarum) dataset of a small farm of size 2.83ha (7 acres) in Kanhegaon village, Ahmednagar (Maharashtra), India.
... In this way nitrogen use efficiency (NUE) can be improved. By applying the nitrogen fertilizers according to the demand of specific soil parts, plants perform uniformly and give maximum and uniform yield [36]. For example, by making a comparison to wheat, corn needs less nitrogen for a given biomass [37]. ...
Article
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It is expected that up to 2050, human population will be doubled. Agricultural researchers are striving their best to meet the food challenges. To get the higher yield, nitrogenous fertilizers use is also being increased. Nitrogenous fertilizers play vital roles in different plant’s growth and developmental processes. But, excessive use of nitrogen is no more beneficial to plants. Only 30 to 50% nitrogen use efficiency is recorded in plants, the remaining nitrogen is used by soil microbes, leached down in soil profile or volatilized. Different agronomical practices have been practiced and suggested for the general cultivation. Proper use of these agronomical practices can increase the crop yield and nitrogen use efficiency.
... partitioning the field into homogenous areas. Recent methods employ clustering algorithms such as fuzzy k-means clustering, to partition the field into homogenous areas based on the internal structure of the data (Fraisse et al., 2001;Fridgen et al., 2004;Kitchen et al., 2005 andFu et al., 2010). While these methods consider the location of the observation (data) in the character space, they do not take into account its geographical spatial context and in particular do not model the spatial relationships between features. ...
Conference Paper
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Management zones partition agricultural fields into sub-units which exhibit homogeneity in yield-defining environmental or plant parameters. Common methods for defining management zones, mainly for field crops, make use of algorithms to partition data observations into clusters based on different similarity methods and often do not account for the spatial neighborhood of the data. Spatial clustering methods, based on spatial statistics, include location of objects and spatial relationships and therefore account for spatial heterogeneity. We present a comprehensive spatial clustering methodology for defining management zones in orchards based on data from individual trees. We have examined the validity of the General G statistic for recognizing global patterns in individual tree data and the Gi* statistic for recognizing local clusters. Results based on case studies on grapefruit in Turkey and plum in Germany demonstrate that point-based spatial-clustering methods and, in particular, the Gi* statistic represent a valid method for delineating management zones in orchards.
... Figure 1B shows high ECa from 117 μS/cm to 313 μS/cm in the southeast section of site two, shown by the dashed black circle. Higher ECa measurements were correlated to higher clay content in clay-pan soils (Kitchen et al. 2005). The variability in near surface soil characteristics within the field was evident from the ECa at both sites. ...
Conference Paper
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Claypan soils cover approximately 40,469 km² in the United States and are characterized by a highly impermeable layer within 0.5 m from the ground surface. This impermeable layer acts as a barrier for infiltrating water, which may increase erosion rates and sediment transport. Two of the main problems associated with these processes are abutment scour and reservoir sedimentation. This study focuses on the undermining of surficial soils due to an impermeable claypan layer in southeastern Kansas. The potential areas of critical soil loss and hydrologic flow patterns were determined using LiDAR-derived digital elevation maps across two 0.45 km² sites. These sites were located in areas of both high and low elevation. Electrical resistivity tomography (ERT) was used in areas identified with LiDAR to measure the depth to claypan, which was originally believed to be uniform across the region. The results indicated that the claypan layer was located from 0.5 to 0.75 m and dissipated moving across the site from an area of high elevation to an area of low elevation. Undisturbed soil samples were collected based on the ERT analysis, in areas with and without the claypan. An erosion function apparatus (EFA) was used to directly measure erosion due to sheet flow and to identify the controlling mechanism causing surficial soil loss. The knowledge gained on claypan erosion mechanisms will improve the prediction of near surface soil erodibility to support aging infrastructure.
... Regarding the dynamic variables, several studies have indicated the importance of soil moisture, soil nitrate, rainfall and temperature on crop yields and EONR in rainfed regions (Andrade et al., 1993). For example, Hergert et al. (1995) and Kitchen et al. (2005) illustrated the importance of spatial and temporal dynamics of soil nitrate on EONR. Ordóñez et al. (2015) and Edreira and Otegui (2013) quantified heat stress effects on corn yield and N uptake. ...
... Management zones are delineated by separating the field into different areas. Some of the areas have different response behaviors, while others may show the same behaviors (Kitchen et al., 2005). Whether areas can be considered to have homogeneous characteristics depends on the situation and is not well known. ...
Article
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Due to spatial variability of soil genesis, topography, and resulting soil properties in farmers’ fields, soil and crop processes vary in space and time. Therefore, optimum rates and timing of resource applications, such as nutrients and irrigation water, may vary as well. It remains a challenge to quantify the spatial variability of a field and to identify effective ways to manage fields in a site-specific manner. The objective of this study was to delineate management zones within a farmer’s field based on relatively easily obtainable information that is statistically integrated. Moreover, soil water temporal dynamics should be evaluated regarding their spatial differences in different zones. The set of direct and indirect observations included clay and silt content, apparent electrical conductivity, soil chemical properties (pH; organic matter; and total N, P, K, Ca, Mg, and Zn), satellite-based normalized difference vegetation index (NDVI), and lidar-based topographic variables in a western Kentucky field. Several key variables and their capability to describe spatial crop yield variability were identified by using principal component analysis: soil clay content, slope, soil organic matter content, topographic wetness index, and NDVI. Two types of cluster analysis were applied to delineate management zones. The cluster analyses revealed that two to three zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences in corn (Zea mays L.) yield and temporally different soil moisture dynamics. The results demonstrate the ability of the proposed procedure to delineate a farmer’s field into zones based on spatially varying soil and crop properties that should be considered for irrigation management.
... A caracterização da variabilidade espacial do solo de uma área agrícola é tradicionalmente realizada a partir de análises laboratoriais de amostras georrreferenciadas, onde é possível que sejam medidos, dentre outros, teores relacionados à textura e matéria orgânica disponível (Mzuku et al., 2005); bem como a partir de parâmetros físicos, tais como resistência a penetração, umidade e condutividade elétrica, cujas medidas são obtidas a partir de sensores de campo (Vaz et al., 2001;Kitchen et al., 2005). Já com relação à variabilidade espacial da cultura, além dos mapas de produtividade e qualidade de frutos (Milne et al., 2012), destaca-se o uso do Índice de Vegetação por Diferença Normalizada (NDVI) (Zhang et al., 2010;Pedroso et al., 2010;Chang et al., 2014). ...
Conference Paper
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Precision Agriculture (AP) has as main goal to increase the profit of a crop, regarding the rational use of agricultural inputs from the identification of the spatial variability of soil attributes and crop. Among these attributes, those that allow the building of maps of vegetal biomass present at some stages of crop growth, such as the Normalized Difference Vegetation Index (NDVI) indicating the presence and vigor of vegetation can significantly help in the identification of the spatial variability of the area. This work describes an experiment to verify the spatial correlation between remote sensing images regarding different spatial resolutions in a grain cultivation area in the state of Mato Grosso. The results showed a good spatial correlation between NDVI maps obtained from better resolution images, such as those provided by the Landsat-8 and Sentinel-2A satellites, indicating the use of this type of map to identify the spatial variability of the crop and, consequently, has great potential for use in different PA activities.
... MZs are deined here as areas within a ield with homogeneous characteristics in regard to soil conditions and landscapes. Traits within an MZ such as similar crop yields, electrical conductivity (EC), and producer-deined areas make zones homogenous [40]. Impact of fertilizers on the environment, input-use eiciency, and yield potential are some of the similarities that the atributes have. ...
... In spatial variability management of fields, the approach based on management zones (MZs) divides the plot into sub-regions, which have topography and soil conditions spatially homogeneous (FLEMING et al., 2004;MORAL et al., 2011;XIN-ZHONG et al., 2009). Such MZs should lead to the same results, such as potential crop yields, allowing a single nutrient input rate in each sub-region (DIACONO et al., 2012;KITCHEN et al., 2005;MILNE et al., 2012;SCHEPERS et al., 2004). ...
Conference Paper
In the management of spatial variability of the fields, the management zone approach (MZs) divides the area into sub-regions of minimal soil and plant variability, which have maximum homogeneity of topography and soil conditions, so that these MZs must lead to the same potential yield. Farmers have experience of which areas of a field have high and low yields, and the use of this knowledge base can allow the identification of MZs in a field based on production history. The objective of this study was to evaluate the use of the farmer's experience in the delineation of MZs. Using data of elevation, soil penetration resistance, sand, silt, clay and soybean yield, the spatial correlation matrix was created to select the layers that influenced the yield. Then, the selected data were interpolated and the MZs were delineated using Fuzzy C-Means clustering method with SDUM (Software for Delineation of Management Zones). The farmer's experience layer was obtained using a mobile application developed for this. The MZs were delineated considering three cases: a) without the use of variable farmer's experience; b) with the variable farmer's experience and stable soil properties selected in the variable selection stage; and c) only with the variable farmer's experience, considering two, three and four sub-regions. The study showed that the use of the farmer's experience to set MZs can be an efficient and simple tool and reduce costs in the MZs setting process when compared to the traditional method of using stable soil variables and the relief. It should be noted that the good results obtained using the farmer's experience variable may have been positively influenced by the farmer's knowledge of this area for a long time.
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Abstract: This study was conducted to examine the capability of topographic features and remote sensing data in combination with other auxiliary environmental variables (geology and geomor- phology) to predict CEC by using different machine learning models ((random forest (RF), k-nearest neighbors (kNNs), Cubist model (Cu), and support vector machines (SVMs)) in the west of Iran. Ac- cordingly, the collection of ninety-seven soil samples was performed from the surface layer (0–20 cm), and a number of soil properties and X-ray analyses, as well as CEC, were determined in the labo- ratory. The X-ray analysis showed that the clay types as the main dominant factor on CEC varied from illite to smectite. The results of modeling also displayed that in the training dataset based on 10-fold cross-validation, RF was identified as the best model for predicting CEC (R2 = 0.86; root mean square error: RMSE = 2.76; ratio of performance to deviation: RPD = 2.67), whereas the Cu model outperformed in the validation dataset (R2 = 0.49; RMSE = 4.51; RPD = 1.43)). RF, the best and most accurate model, was thus used to prepare the CEC map. The results confirm higher CEC in the early Quaternary deposits along with higher soil development and enrichment with smectite and vermiculite. On the other hand, lower CEC was observed in mountainous and coarse-textured soils (silt loam and sandy loam). The important variable analysis also showed that some topographic attributes (valley depth, elevation, slope, terrain ruggedness index—TRI) and remotely sensed data (ferric oxides, normalized difference moisture index—NDMI, and salinity index) could be considered as the most imperative variables explaining the variability of CEC by the best model in the study area.
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The global wine industry has recognized the benefits of embracing a more sustainable approach to be more competitive and resilient. However, evaluation of complex winegrowing systems and assessment of their sustainability performance remains a difficult task. In this paper, we use the Douro wine region in Portugal as a case study to test different benchmarking approaches to assess sustainability performance. Our findings revealed a greater sustainability of social and environmental components of the Douro vineyard farms, but the economic pillar shows weaknesses. We may assume that the Douro region demands a strategy to improve its economic stability, resilience, and adaptability to face future challenges (more adverse climate conditions, labor shortage, and increased costs). This work also emphasizes the need of the wine sector to increase and improve research on sustainability issues and evaluation frameworks to assess it, having always present the context where the winegrowing system operates.
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The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.
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This book provides an in-depth coverage of the most recent developments in the field of wireless underground communications, from both theoretical and practical perspectives. The authors identify technical challenges and discuss recent results related to improvements in wireless underground communications and soil sensing in Internet of Underground Things (IOUT). The book covers both existing network technologies and those currently in development in three major areas of SitS: wireless underground communications, subsurface sensing, and antennas in the soil medium. The authors explore novel applications of Internet of Underground Things in digital agriculture and autonomous irrigation management domains. The book is relevant to wireless researchers, academics, students, and decision agriculture professionals. The contents of the book are arranged in a comprehensive and easily accessible format. • Focuses on fundamental issues of wireless underground communication and subsurface sensing; • Includes advanced treatment of IOUT custom applications of variable-rate technologies in the field of decision agriculture, and covers protocol design and wireless underground channel modeling; • Provides a detailed set of path loss, antenna, and wireless underground channel measurements in various novel Signals in the Soil (SitS) testbed settings.
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Delineation 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 analyses, 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.
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The study aims at spatial analysis of water deficit of fruit trees under semi-humid climate conditions. Differences of soil, root, and their relation with the spatial variability of crop evapotranspiration (ETa) were analyzed. Measurements took place in a six hectare apple orchard (Malus x domestica ‘Gala’) located in fruit production area of Brandenburg (latitude: 52.606°N, longitude: 13.817°E). Data of apparent soil electrical conductivity (ECa) in 25 cm were used for guided sampling of soil texture, bulk density, rooting depth, root water potential, and volumetric water content. Soil ECa showed high correlation with root depth. The readily available soil water content (RAW) was calculated considering three cases utilizing (i) uniform root depth of 1 m, (ii) measured values of root depth, and (iii) root water potential measured during full bloom, fruit cell division stage, at harvest. The RAW set the thresholds for irrigation. The ETa was calculated based on data from a weather station in the field and RAW cases in high, medium and low ECa conditions. ETa values obtained were utilized to quantify how fruit trees cope with spatial soil variability. The RAW-based irrigation thresholds for locations of low and high ECa value differed. The implementation of plant parameters (rooting depth, root water potential) in the water balance provided a more representative figure of water needs of fruit trees Consequently, the precise adjustment of irrigation including plant data can optimize the water use.
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Zone sampling for site-specific N application has been shown to be effective in North Dakota and other areas of the Great Plains. Printed and sometimes digitized soil surveys are presently available for most agricultural counties in the USA. Order 2 soil surveys generally have scales that range from 1:12 000 to 1:31 680. These surveys were developed for general planning purposes. There is interest in using Order 2 soil surveys as a basis for delineating N management zone patterns, especially where the soil-mapping units have been digitized. This study was conducted to evaluate soil survey scales at the Order 1 (scale >1:15 840) and Order 2 level against grid- and topography-based zone sampling to determine whether soil surveys at these scales could be used to delineate N management zones for site-specific fertilizer application. Fields mapped at a finer scale (Order 1 survey) showed some similarity between mapping units and N management zones defined by topography. Order 2 soil-mapping units, which are the present mapping scale of most agricultural soil surveys, were often not similar to N management zones. Published Order 2 soil surveys should not be used to develop N management zones for site-specific agriculture unless the soil patterns are verified with other zone development tools of site-specific management. Alternatively, a major benefit of Order 1 soil surveys would be to reinforce or redefine apparent N management zones.
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Irrigated agriculture is necessary to meet the food demands of the world, but excessive irrigation has wasted water and drainage from it has degraded the productivity and altered the ecology of vast areas of land. Irrigated agriculture also has polluted associated surface water and groundwater resources. The extent of soil degradation from salinization and waterlogging and, especially, the extent of water salinization resulting from excessive irrigation have not been well quantified. Additionally, the diffuse sources of deep percolation and salt loading from irrigation have not been well established. This paper describes basic principles of soil electrical conductivity, recent technology developed to assess the magnitude and distribution of soil salinity in fields, and ways to infer the areal sources and amounts of diffuse salt loading from irrigation.
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The ability to assess through prognostication the impact of non-point source (NPS) pollutant loads to groundwater, such as salt loading, is a key element in agriculture's sustainability by mitigating deleterious environmental impacts before they occur. The modeling of NPS pollutants in the vadose zone is well suited to the integration of a geographic information system (GIS) because of the spatial nature of NPS pollutants. The GIS- linked, functional model TETrans was evaluated for its ability to predict salt loading to groundwater in a 2396 ha study area of the Broadview Water District located on the westside of central California's San Joaquin Valley. Model input data were obtained from spatially-referenced measurements as opposed to previous NPS pollution modeling effort's reliance upon generalized information from existing spatial databases (e.g., soil surveys) and transfer functions. The simulated temporal and spatial changes in the loading of salts to drainage waters for the study period 1991-1996 were compared to measured data. A comparison of the predicted and measured cumulative salt loads in drainage waters for individual drainage sumps showed acceptable agreement for management applications. An evaluation of the results indicated the practicality and utility of applying a one-dimensional, GIS-linked model of solute transport in the vadose zone to predict and visually display salt loading over thousands of hectares. The display maps provide a visual tool for assessing the potential impact of salinity upon groundwater, thereby providing information to make management decisions for the purpose of minimizing environmental impacts without compromising future agricultural productivity.
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Crop yields are frequently heterogeneous across space and time. We performed this study to determine if cluster analysis could be used to decipher the temporal and spatial patterns of corn (Zea mays L.) yield within a field. Nonhierarchal cluster analysis was applied to 6 yr of corn yield data collected for 224 yield plots on a regular grid on the southern half of a 32-ha field. We were able to group the yield observations into five temporal yield patterns or clusters. The clusters were not randomly distributed across the field but instead formed contiguous areas roughly equivalent to landscape positions. Cluster membership was determined primarily by yield differences in years with growing season precipitation greater than the 40-yr average. A multiple discriminant analysis was used to predict the spatial occurrence of the clusters from easily determined field attributes: soil electrical conductivity, elevation, slope, and plan and profile curvature. The multiple discriminant functions were unable to distinguish between the two clusters located on the lowest portions of the landscape. Because of similar temporal yield patterns in these two clusters, they were combined and the multiple discriminant analysis repeated for four clusters. Using a holdout sample approach, we achieved 76 and 80% success rates in classifying the yield plots into the correct yield clusters. If response curves for inputs such as N prove to be unique for the different yield clusters, then clustering of multiple-year yield data may prove an effective method for determining management zones within fields.
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Reluctance towards implementation of precision agriculture seems to be based upon accessibility to well-trained, knowledgeable people, and the cost and availability to obtain quality education, training, and products. Given that precision agriculture is rapidly changing and the current trend for accelerated information exchange, educators of precision agriculture face the challenge of keeping pace and providing quality educational programs. This paper addresses how precision agriculture educational programs can be improved. Specific barriers to adoption of precision agriculture are discussed. The learning process of precision agriculture technologies and methods are outlined as six sequential steps. These steps represent a process of increased learning and skill proficiency against which those individuals developing precision agriculture education can use to build and target their programs. The optimal value of information for precision agriculture will be best achieved by producers, agribusinesses, and educators as they improve their: 1) agronomic knowledge and skills, 2) computer and information management skills, and 3) understanding of precision agriculture as a system for increasing knowledge.
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As precision agriculture strives to improve the management of agricultural industries, the importance of scientific validation must not be forgotten. Eventually, the improvement that is imparted by precision agriculture management must be considered in terms of profitability and environmental impact (both short and long term). As one form of precision agriculture, we consider site-specific crop management to be defined as: Matching resource application and agronomic practices with soil and crop requirements as they vary in space and time within a field. While the technological tools associated with precision agriculture may be most obvious, the fundamental concept will stand or fall on the basis of scientific experimentation and assessment. Crucial then to scientifically validating the concept of site-specific crop management is the proposal and testing of the null hypothesis of precision agriculture, i.e. Given the large temporal variation evident in crop yield relative to the scale of a single field, then the optimal risk aversion strategy is uniform management. The spatial and temporal variability of important crop and soil parameters is considered and their quantification for a crop field is shown to be important to subsequent experimentation and agronomic management. The philosophy of precision agriculture is explored and experimental designs for Precision agriculture are presented that can be employed in attempts to refute the proposed null hypothesis.
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The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT–CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT–CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.
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The productive capacity of a soil is generally related to topsoil depth. This study was conducted to determine the influence of topsoil depth, soil fertility, and water management on grain yield for corn (Zea mays L.) and soybean (Glycine max [L.] Merr.). A field experiment at Columbia, MO, was initiated in 1983 and maintained through 1987 on a Mexico silt loam (fine, montmorillonitic, mesic Udollic Ochraqualf). The study was conducted using a split-plot experiment for each crop. Main effects included topsoil (depth to claypan), soil fertility, and water management. Desurfaced plots were constructed having topsoil depths of 0, 125, 250, and 375 mm. Plots were either fertilized according to soil-test recommendations or unfertilized. Water management was either rain fed or with supplemental irrigation. Overall, corn yield was 5.7 times more sensitive than soybean to topsoil depth when averaged over both fertility and water management. The topsoil x soil fertility interaction was characterized by a 34% reduction in corn yield with topsoil depths < 125 mm in the fertilized treatments when averaged across water management. Similar yield reductions for unfertilized plots were observed but were linearly related to topsoil depth. The fertility x irrigation interaction described a multiplicative increase in corn yield when corn was both fertilized and irrigated. Only topsoil depth and water management significantly influenced overall soybean yield.
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The objective of this research was to determine if unsupervised classification of topographic attributes and soil electrical conductivity could identify management zones for use in precision agriculture. Data collected in two fields located in central Missouri were used to test the proposed methodology. Principal component analysis was used to determine which layers of data were most important for representing within-field variability. Unsupervised clustering algorithms implemented in geographic information system (GIS) software were then used to divide the fields into potential management zones. Grain yield data obtained using a full-size combine equipped with a commercial yield sensing system and global positioning system (GPS) receiver were used to analyze the "goodness" of the potential management zones defined for each field. Principal component analysis of input variables for Field 1 indicated that elevation and bulk soil electrical conductivity (EC) were more important attributes than slope and Compound Topographic Index (CTI) for defining claypan soil management zones. The optimum number of zones to use when dividing a field may vary from year to year and was mainly a function of weather and the crop planted. The number of zones decreased if adequate moisture conditions were present throughout the cropping season (unpredictable) or if crops tolerant to water stress were planted (predictable). This classification procedure is fast, can be easily automated in commercially available GIS software, and has considerable advantages when compared to other methods for delineating within-field management zones.
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Crop simulation models have historically been used to predict field average crop development and yield under alternative management and weather scenarios. The objective of this research was to calibrate and test a new version of the CERES-Maize model, modified to improve the simulation of site-specific crop development and yield. Seven sites within a field located in central Missouri were selected based on landscape position, elevation, depth to a claypan soil horizon, and past yield history. Detailed monitoring of crop development and soil moisture during the 1997 season provided data for calibration and evaluation of model performance at each site. Mid-season water stress caused a large variation in measured yield with values ranging from 2.6 Mg ha(-1) in the eroded side-slope areas to 10.1 Mg ha(-1) in the deeper soils located in the low areas of the field. The model was calibrated against measured data for root zone soil moisture content, leaf area index, and grain yield. The results demonstrated that modifications included in the model to simulate root growth and development are important in soils with a high-clay restrictive layer such as the claypan soils. Although the model performed well in simulating yield variability, simulated leaf area indices were below measured values at five out of seven monitoring sites, suggesting a need for model improvements. Results showed that accurate simulation of crop growth and development for areas of the study field that receive run-on or subsurface flow contributions from upland areas will require enhancement of the model to account for the effects of these processes.
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Many producers who map yield want to know how soil and landscape information can be used to help account for yield variability and provide insight into improving production. This study was conducted to investigate the relationship of profile apparent soil electrical conductivity (ECa) and topographic measures to grain yield for three contrasting soil-crop systems. Yield data were collected with combine yield-monitoring systems on three fields [Colorado (Ustic Haplargids), Kansas (Cumuic Haplustoll), and Missouri (Aeric Vertic Epiaqualfs)] during 1997-1999. Crops included four site-years of corn (Zea mays L.), three site-years of soybean (Glycine max L.), and one site-year each of grain sorghum [Sorghum bicolor (L.) Moench] and winter wheat (Triticum aestivum L.). Apparent soil electrical conductivity was obtained using a Veris model 3100 sensor cart system. Elevation, obtained by either conventional surveying techniques or real-time kinematic global positioning system, was used to determine slope, curvature, and aspect. Four analysis procedures were employed to investigate the relationship of these variables to yield: correlation, forward stepwise regression, nonlinear neural networks (NNs), and boundary-line analysis. Correlation results, while often statistically significant, were generally not very useful in explaining yield. Using either regression or NN analysis, ECa alone explained yield variability (averaged over sites and years R2 = 0.21) better than topographic variables (averaged over sites and years R2 = 0.17). In six of the nine site-years, the model R2 was better with ECa than with topography. Combining ECa and topography measures together usually improved model R2 values (averaged over sites and years R2 = 0.32). Boundary lines generally showed yield decreasing with increasing ECa for Kansas and Missouri fields. Results of this study can benefit farmers and consultants by helping them understand the degree to which sensor-based soil and topography information can be related to yield variation for planning site-specific management.
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Producers using site-specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones. Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.
Conference Paper
A robust new approach for describing and segmenting landforms that is directly applicable to precision farming has been developed in Alberta. The model uses derivatives computed from DEMs and a fuzzy rule base to identify up to 15 morphologically defined landform facets. The procedure adds several measures of relative landform position to the widely used classification of Pennock et al., (1987). The original 15 facets can be grouped to reflect differences in complexity of the area or scale of application. Research testing suggests that a consolidation from 15 to 3–4 units provides practical, relevant separations at a farm field scale. These units are related to movement and accumulation of water in the landscape and are significantly different in terms of soil characteristics and crop yields. The units provide a base for benchmark soil testing, for applying biological models and for developing agronomic prescriptions and management options. Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © 1999. . Copyright © 1999 American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America Inc., 5585 Guilford Rd., Madison, WI 53711 USA
Chapter
Over the last decades much effort has been invested in the development of complex simulation models, incorporating and integrating the current understanding of soil–water–plant interactions. The use of such models in precision agriculture has been shown to have great potential. This research presents a methodology to derive basic units for precision agriculture, referred to as management units. Their main purpose is to reduce the theoretically infinite variability of growth conditions in the field to a limited set, which can be evaluated using mechanistic models. A quantitative criterion is applied to ensure that management units accurately represent local variation with respect to growth conditions. Using representative soil profiles for each management unit, real-time simulations can provide insight in crop performance and the nutrient status of the soil. This information can be used to optimise farm management, maintaining crop performance while reducing environmental impacts. Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © 1999. . Copyright © 1999 American Society of Agronomy, Inc., Crop Science Society of America, Inc., Soil Science Society of America Inc., 5585 Guilford Rd., Madison, WI 53711 USA
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In an area of Mexico soils in central Missouri, a high correlation between the observed depth to claypans and the response of the EM38 meter was observed. Equations were developed to infer depths and chart the topography of the claypan. Compared with traditional methods of observing this subsurface layer, electromagnetic techniques are noninvasive, less labour intensive, more economical and can produce large quantities of data in a relatively short period of time. -from Authors
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Technologies to support precision farming (PF) began to emerge in 1989 when the Global Positioning System (GPS) became available to a limited extent and was tested as a means for locating farm equipment within fields. Substantial PF technology is available with rapidly decreasing costs and increasing capabilities. However, one major class of information that is missing is a method for determining how much material to apply or what action to take as a result of a specific condition at any position within a field. Developing this information will require knowing the spatial and temporal variability of plant response and will most likely be obtained by measuring yield variability. This field study was designed to quantify yield variability within a 16 ha field which has had consistent practices for several years. Crop yields showed a coefficient of variation ranging from near 12% in 1989 and 1992 to over 30% in 1990 and 1993. Rankings of the long-term relative yield for 224 locations were not stable even after 6 years when recalculated each year. Many PF scenarios are based on the assumption of a stable yield pattern within a field, but only a few points in this field have exhibited such a pattern. Perhaps stable patterns will eventually emerge, but the time frame for this to occur may be quite long. Overall, this study suggests that implementation of PF practices within the Clarion-Nicollet-Webster soil association area will reveal both differences and opportunities.
Article
Technologies to support precision farming (PF) began to emerge in 1989 when the Global Positioning System (GPS) became available to a limited extent and was tested as a means for locating farm equipment within fields. Substantial PF technology is available with rapidly decreasing costs and increasing capabilities. However, one major class of information that is missing is a method for determining how much material to apply or what action to take as a result of a specific condition at any position within a field. Developing this information will require knowing the spatial and temporal variability of plant response and will most likely be obtained by measuring yield variability. This field study was designed to quantify yield variability within a 16 ha field which has had consistent practices for several years. Crop yields showed a coefficient of variation ranging from near 12% in 1989 and 1992 to over 30% in 1990 and 1993. Rankings of the long-term relative yield for 224 locations were not stable even after 6 years when recalculated each year. Many PF scenarios are based on the assumption of a stable yield pattern within afield, but only a few points in this field have exhibited such a pattern. Perhaps stable patterns will eventually emerge, but the time frame for this to occur may be quite long. Overall, this study suggests that implementation of PF practices within the Clarion-Nicollet-Webster soil association area will reveal both difficulties and opportunities.
Article
Inexpensive and accurate methods for spatially measuring soil properties are needed that enhance interpretation of yield maps and improve planning for site-specific management. This study was conducted to investigate the relationship of apparent profile soil electrical conductivity (EC(a)) and grain yield on claypan soils (Udollic Ochraqualfs). Grain yield data were obtained by combine yield monitoring and EC(a) by a mobile, on-the-go electromagnetic (EM) induction meter. Investigations were made on four claypan fields between 1993 and 1997 for a total of 13 site-years. Crops included five site-years of corn (Zea mays L.), seven site-years of soybean [Glycine max (L.) Merr.], and one site-year of grain sorghum [Sorghum bicolor (L) Moench]. Transformed EC(a) (1/EC(a)) was regressed to topsoil thickness giving r2 values > 0.75 for three of the four fields. The relationship between grain yield and EC(a) was examined for each site-year in scatter plots. A boundary line using a log-normal function was fit to the upper edge of data in the scatter plots. A significant relationship between grain yield and EC(a) (boundary lines with r2 > 0.25 in nine out of 13 site-years) was apparent, but climate, crop type, and specific field information was needed to explain the shape of the potential yield by EC(a) interaction. Boundary line data of each site-year fell into one of four condition categories: Condition 1 - site-years where yield increased with decreasing EC(a); Condition 2 - site-years where yield decreased with decreasing EC(a); Condition 3 - where yield was less at low and high EC(a) values and highest at some mid-range values of EC(a); and Condition 4 - site-years where yield variation was mostly unrelated to EC(a). Soil EC(a) provided a measure of the within-field soil differences associated with topsoil thickness, which for these claypan soils is a measure of root-zone suitability for crop growth and yield.
Article
In variable rate application technology (VRT), crop production input rate is changed within fields in response to spatially variable factors that affect the optimum application rate. It is being considered by farmers and the crop input industry because factors that affect crop yield are not always uniform within fields and, therefore, do not allow optimum efficiency or profitability from uniform application. If VRT is widely adopted, then a change must occur in the predominant management practice of applying inputs as uniformly as possible and changing rates only between fields-what has historically occurred on the majority of production fields. Properly implemented, VRT is a correct concept and can work [...]
Article
A soil map is one of the key data layers for developing a robust global model and evaluating land quality and use. A current soil map produced by conventional soil survey is the major source of soil information. However, such a map may not provide the desired accuracy in terms of scale and cartographic quality as a digital format for geographic information system (GIS) modeling applications. This study was designed to introduce and test the procedures for improving the objectivity and accuracy in the delineation of soil patterns with the use of hyperspectral imagery. These hyperspectral data were analyzed through different models including the linear mixture model, block-kriging interpolation, and fuzzy-c-means (FCM) algorithms. Hyperspectral remote sensing data, having very good spectral and spatial resolution, were used for quantifying soil patterns and conditions. A linear spectral mixing model was effectively used not only for reducing dimensionality but also for removing vegetation effects for studying soil patterns from a single soil map layer derived from hyperspectral remote sensing data. Block kriging interpolation based on a semivariogram tilted with the isotropic exponential model represented soil patterns very well beyond the limitation of the size of pixel. Fuzzy-c-means clustering analysis showed clear membership patterns and segmented soil patterns effectively, although this is not a soil map in the conventional sense.
Article
Annual yield maps are spatially fragmented because of random variation caused by crop management as well as measurement errors. Two approaches for creating maps of spatially contiguous yield classes were evaluated at two irrigated sites. In the first approach, prior-classification interpolation (PCI), grid size was increased from 4, 8, 16, and 32 to 64 m by kriging interpolation before cluster analysis used for mapping yield classes. Choosing a coarse resolution (>16 m) for yield interpolation before spatial classification resulted in maps that did not accurately depict yield patterns, significant decline of the yield variance accounted for, and loss of resolution in areas of sharp yield transitions caused by irrigation or near the field borders. In the second approach, postclassification filtering (PCF), cluster analysis of mean relative yield was conducted on the smallest grid size (4 m), and the classification results were postprocessed using a spatial filtering algorithm with window sizes that were equivalent to the 8-, 16-, 32-, and 64-m grid sizes used in PCI. This procedure removed erroneous map fragmentation and created maps of contiguous yield classes while preserving the class means and general yield patterns at high spatial resolution. Window sizes for spatial filtering of yield maps should be in the 30- to 60-m range. Landscape pattern metrics may offer new potential for assessing mapping techniques as well as comparing agricultural production fields with regard to ranking their relative opportunities for site-specific crop management.
Article
Producers using site-specific crop management (SSCM) have a need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management zones. Quick and automated procedures are desirable for creating management zones and for testing the question of the number of zones to create. A software program called Management Zone Analyst (MZA) was developed using a fuzzy c-means unsupervised clustering algorithm that assigns field information into like classes, or potential management zones. An advantage of MZA over many other software programs is that it provides concurrent output for a range of cluster numbers so that the user can evaluate how many management zones should be used. Management Zone Analyst was developed using Microsoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are presented as are the sequential steps of the program. Management Zone Analyst calculates descriptive statistics, performs the unsupervised fuzzy classification procedure for a range of cluster numbers, and provides the user with two performance indices [fuzziness performance index (FPI) and normalized classification entropy (NCE)] to aid in deciding how many clusters are most appropriate for creating management zones. Example MZA output is provided for two Missouri claypan soil fields using soil electrical conductivity, slope, and elevation as clustering variables. Management Zone Analyst performance indices indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be divided into four (using NCE or FPI) management zones.
Article
This paper uses fuzzy classification to determine land suitability and compares the results obtained with conventional Boolean methods. The methods are illustrated using data from the Alberta Agricultural Department experimental farm at Lacombe in Alberta, Canada. Data on site elevation and soil chemical and physical properties measured at 154 soil profiles were interpolated by ordinary block kriging to 15 m × 15 m cells on a 50 × 50 grid. The digital elevation model created by interpolating the elevation data was used to determine the surface drainage network and map it in terms of the numbers of cells draining through each cell on the grid. This map was reclassified to yield Boolean and fuzzy maps of surface wetness which were then intersected with the soil profile classes. Fuzzy methods produce contiguous areas and reject less information at all stages of the analyses than Boolean methods. They are much better than Boolean methods for classification of continuous variation, such as the results of the drainage analysis. -from Authors
Article
Of the various factors that affect crop yield, soil water-holding capacity is usually a significant contributor. Soil electrical conductivity (EC) measurements in non-saline soils are driven primarily by soil texture and soil moisture. Those same factors correlate highly to the soil's water-holding capacity. Thus, an EC map can serve as a proxy for soil water-holding capacity, resulting in soil EC and yield maps that frequently exhibit similar spatial patterns. Numerous commercial EC mapping systems are being used in precision agriculture, and many of the maps generated by these units are being layered in a GIS with yield data in an attempt to explain yield variability. A common tool being employed in yield-EC analyses is bi-variate linear regression. While this analysis frequently explains a larger percentage of yield variability than is explained by other available layers of soil sample information, it ignores the more complex relationships between soil physical properties and yield. Moving to a non-linear curve-fit may improve the correlation co-efficient but rarely explains more than 50% of the yield variability within a field. This paper presents an analysis technique that sorts through the cloud of yield data points to establish a yield benchmark for each soil EC level. Further analysis generates maps that can be used to investigate areas that are performing below the benchmark.
Article
The site-specific application of inputs such as seed, fertilizer and crop protection chemicals has the potential to reduce input costs, maximize yields, and benefit the environment. The economic returns currently received by the early adopters of precision farming methods need to be improved before wide-scale acceptance of this practice will occur. These improvements include cost-effective identification and management of the spatial variability of soil and nutrients, applying inputs based on each site's productive capacity, and correct decision-making using the available layers of information. Soil electrical conductivity (EC) measurements have long been used to identify contrasting soil properties in the geological and environmental fields. The purpose of this paper is to discuss the applications in precision farming where EC maps are proving useful in improving economic returns to precision farming. The usefulness of soil conductivity stems from the fact that sands have a low conductivity, silts have a medium conductivity and clays have a high conductivity. Consequently, conductivity (measured at low frequencies) correlates strongly to soil grain size and texture. One of the applications of an EC map, which will be covered in this paper, is to view it in conjunction with other information layers. Farmers practicing precision farming methods have a wide assortment of data layers to assist them in their decision-making: yield maps, fertility/grid sampling data, soil surveys, a grower's historical knowledge, and visible changes in soil appearance and topography. As soil serves as the growth medium for crops, it is a major factor affecting a crop's potential. This paper provides examples of how an EC soil map, along with other information layers, can offer a promising foundation for precision farming.
Article
A field-level geographic information system (FIS) designed specifically for research in precision agriculture has been under development at Kansas State University for several years. This article summarizes the analytical functions provided by FIS and gives two examples to illustrate its applications in precision agriculture. The first example studies yield response to soil electric conductivity using mathematical/logic query and simple statistics functions. The second example demonstrates two methods for delineating management zones. The first method uses the buffer function of FIS to form morphological opening and closing filters. The second method is based on spectral analysis of the grid maps. Low-pass filters in the frequency domain and query functions are used to delineate the management zones. The management zones derived using the two methods were similar.