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A study was conducted at Kalamandari Tanda-1 micro-watershed, Karnataka, India, to assess the spatio-temporal variability of surface soil properties sampled from 23 varied soil-phase units, delineated using IRS P6 LISS-IV merged Cartosat-I imagery (2.5 m spatial resolution). Three sets of surface soil samples (231 in each set) were collected, analyzed, and interpolated to evaluate the soil spatio-temporal variability. Further soil-phase-wise composite representative samples (23 in each set) were collected before the sowing and after the harvest of pigeon peasin January 2019 and 2020. Results showed that the soil OC with N, Mg with Ca, exchangeable sodium percentage (ESP), and cation exchange capacity (CEC) with Mn and K2O have a significantly positive association (p < 0.01) during both years. Similarly, a significantly positive association (p < 0.05) was observed among soil OC and N with Cu, Mn with CEC, and Zn with Fe, while a negative association was seen between available P2O5 and Na. Skewness from descriptive statistics revealed that pH, EC, Cu, Mg, B, and ESP distribution varied temporally, whereas other parameters remained almost unchanged. Geo-statistically, the spatial distribution of measured soil properties was best fitted to exponential, stable, and K-Bessel models. The available N in 2018 and the ESP in 2019 have shown weak spatial dependency, whereas the rest of the soil parameters showed moderate and strong spatial dependency. These interpolated maps were exported as vector layers to quantify soil-phase-wise spatio-temporal variability of soil properties. [ABSTRACT FROM AUTHOR]

Terroir is not just a geographical site, but also a complex concept aiming to express the “collective knowledge of the interactions” between the environment and the vines mediated through human action, “providing distinctive characteristics” to the final product (OIV 2010). In the popular press, it is often treated and communicated without a proper understanding of the mechanistic relationships between the wine characteristics and the site. These relationships are primarily rooted in the physical environment, particularly in the interactions between the soil-plant and atmosphere system, affecting grapevine physiology, grape composition and wine. Comprehension of the phenomena starts with viticulture zoning techniques, a crucial first step in mapping, describing and further studying terroirs. Viticulture zoning can be carried out with diverse empiricism and expertise and achieving different level of details in describing complex biophysical processes. Spatial and temporal scales can vary across studies, and not all of them have been able to capture the multidisciplinary nature of the terroir. The scientific understanding of the mechanisms ruling vineyard variability and grape composition is one of the most critical scientific focuses of terroir research. This knowledge can contribute to the analysis of climate change impacts on terroir resilience, the identification of new suitable land for viticulture, and the precise management of vineyards to reach a specific oenological goal. This article gives an overview of the latest approaches to terroir studies and of new zoning technology, with particular attention to their importance in supporting terroir resilience to climate change.

Site specific management rationalizes farm inputs and mitigate environmental impacts. Traditionally, low resolution satellite imagery and soil maps are employed for site specific decisions in large scale farms. However, these approaches are not good at sub-field level due to low spatial resolution. To overcome this problem, either manual scouting is employed or extensive high resolution data collection platforms are used. In both cases, the cost outweighs the expected returns. Consequently, variable rate applications are not preferred in large fields. Leaf Area Index (LAI) is a useful measure to monitor crop growth and health for site specific management. In this paper we propose an accurate and scalable process where multispectral remote sensing and proximal sensing data is used to estimate LAI. Crop LAI (CLAI) and Weed LAI (WLAI) are estimated from limited high resolution ground image samples using semantic segmentation. These limited LAIs are extended to the whole field using remote sensing and proximal sensing data. We find that LAIs are spatially related with Soil, Water and Topography (SWAT) maps and are field specific. With increasing weed population in the fields, correlation of WLAI with the SWAT zone increases. However, CLAI remains comparatively consistent across SWAT zones due to variable rate seeding and fertilizer application based on soil variance. Our results demonstrate that LAIs can be predicted accurately from limited high resolution ground imagery, satellite imagery, SWAT, and soil properties maps.

Quantification of nitrogen mineralization at the field scale is necessary to improve current N fertilizer recommendations. The objective of this study was to identify a suitable set of explanatory variables and an appropriate modelling framework to estimate the average daily amount of N mineralized (DANM) during a corn (Zea mays L.) growing season. In six field experiments in Flat Inland Pampas, Argentina, DANM was evaluated in three different homogeneous zones (HZ), which were defined based on terrain elevation. Mixed‐effect generalized linear models were developed that included growing season as random effect as well as HZ, sand, clay, silt, soil organic carbon, and normalized difference vegetation index (NDVI) as fixed effects. Results showed that DANM ranged from 0.19 to 1.25 kg N ha−1 day−1 with a mean of 0.64 kg N ha−1 day−1, which is equivalent to 110 kg N ha−1 for the corn growing season. The best model included HZ, sand, and early NDVI measurements, and predicted DANM with an RMSE of 0.19 kg N ha−1 day−1. Thus, the model should be improved and tested in the Inland Flat Pampas before it could be used to assess N fertilizer application rates. [ABSTRACT FROM AUTHOR]

In present study, geostatistical techniques were applied to assess the spatial variability of DTPA-extractable micronutrients which are named heavy metals as chemistry such as; iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in the non-tillaged layer in Ordu province- Altınordu district, Black sea region, Eastern part of Turkey. Study area was approximately 40095.8 ha where was divided into grids with 2500 x 2500 m spacing with including 66 sampling points from 0-0.2 m in depth. Soil reaction (pH) was the least variable property while electrical conductivity (EC) was the most variable. While the highest nugget effect occurred for Ext-Cu with moderate spatial dependence, the lowest for Ext-Mn with strong spatial dependence. The greatest range of influence (17424 m) occurred for Ext-Cu and the least range (692 m) for Ext-Zn.

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land degradation due to secondary soil salinization. This study demonstrates the use of multi-temporal, high spatial resolution (10 m) SPOT-4/5 image data in an integrated change vector analysis and spectral mixture analysis (CVA-SMA) procedure. This procedure was implemented with the principal objective of mapping sub-field vegetation cover dynamics in irrigated lowland areas within the lowerlands of the Amu Darya River. CVA intensity and direction were calculated separately for the periods of 1998–2006 and 2006–2010. Cumulative change intensity and the overall directional trend were also derived for the entire observation period of 1998–2010. Results show that most of the vector changes were observed between 1998 and 2006; persistent conditions were seen within the study region during the 2006–2010 period. A decreasing vegetation cover trend was identified within 38% of arable land. Areas of decreasing vegetation cover were located principally in the irrigation system periphery where deficient water supply and low soil quality lead to substandard crop development. During the 2006–2010 timeframe, degraded crop cover conditions persisted in 37% of arable land. Vegetation cover increased in 25% of the arable land where irrigation water supply was adequate. This high sub-field crop performance spatial heterogeneity clearly indicates that current land management practices are inefficient. Such information can provide the basis for implementing and adapting irrigation applications and salt leaching techniques to site-specific conditions and thereby make a significant contribution to sustainable regional land management.

Precision agriculture (PA) has been used for ≥25 years to optimize inputs, maximize profit, and minimize negative environmental impacts. Legumes play an important role in cropping systems, by associating with rhizobia microbes that convert plant-unavailable atmospheric nitrogen into usable nitrogen through symbiotic nitrogen fixation (SNF). However, there can be field-level spatial variability for SNF activity, as well as underlying soil factors that influence SNF (e.g., macro/micronutrients, pH, and rhizobia). There is a need for PA tools that can diagnose spatial variability in SNF activity, as well as the relevant environmental factors that influence SNF. Little information is available in the literature concerning the potential of PA to diagnose/optimize SNF. Here, we critically analyze SNF/soil diagnostic methods that hold promise as PA tools in the short--medium term. We also review the challenges facing additional diagnostics currently used for research, and describe the innovations needed to move them forward as PA tools. Our analysis suggests that the nitrogen difference method, isotope methods, and proximal and remote sensing techniques hold promise for diagnosing field-level variability in SNF. With respect to soil diagnostics, soil sensors and remote sensing techniques for nitrogen, phosphorus, pH, and salinity have short--medium term potential to optimize legume SNF under field conditions. [ABSTRACT FROM AUTHOR]

The site specific management of variable rate nitrogen (N) fertiliser application to crops is a cost-effective system that optimises outputs and reduces environmental impact. However, its implementation requires information on the spatial variability of soil and crop variables and, especially, of the N supply from the soil, measured as the available N and the N mineralized from organic matter. The objective of this study was to obtain the spatial structure of the variation of net N mineralization, within the field scale in a cereal cropping system, in order to improve site specific N management. A nested sampling survey was conducted in the field using scales of variation at 1.5, 4.5, 13.5, 40.5 and 121.5 m, arranged in hierarchical order with n = 96 samples. Samples were collected in autumn and spring and N mineralization measured by aerobic incubation. The components of variance of the N mineralized were calculated using residual maximum likelihood and used to produce an approach to the variogram. The within-field spatial variation was almost all (92-93%) encompassed by the scales of variation measured, all occurring within 40.5 m in both seasons. However, there was a significant amount of fine scale variation at 1.5 m in autumn and 4.5 m in spring. These results will guide future spatial sampling of the N supply, and soil monitoring in general. [ABSTRACT FROM AUTHOR]

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• Grain crops nitrogen status was monitored UAV and a multispectral camera using deep. learning approaches. • Attention R2U-Net was fine-tuned and optimized as the best a robust and reliable for. different kinds of crops with accuracy 97.59–99.96%. • The combination of RGB, NearIR, and NDVI bands was recognized as the best. combination of different image bands for grain crops nitrogen segmentation. • Special augmentation procedure was developed to generate synthetic data of an agricultural field for training neural network models. Effective nitrogen nutrition is vital for better crop yield. In order to get the maximum yield from a field, nutrition must be spread evenly among all crops. Therefore, this paper proposes a combination of deep learning image segmentation methods to monitor nutrition across an agricultural field and detect areas with shortages of nutrients. In particular, the authors consider the applicability of five state-of-the-art neural network architectures based on U-Net to solve the nitrogen level rate segmentation problem for crops on an orthophotomap. Training, effectiveness assessment, and applicability of these neural network models are carried out by the authors on their own multi-datasets, collected by using UAS (Geoscan 401) at the Agrophysical Research Institute (ARI) experimental biopolygon for 2020–2021. The survey was performed using a MicaSense RedEdge-MX multispectral camera (5 channels in total). The total size of the collected dataset is more than 20 thousand images of two different agricultural fields (with a total area of about 62 ha). On each field, there are six test areas with known nitrogen nutrition levels (founded by agronomists). Images of these test areas are used for data augmentation and training of the above-mentioned neural network models (U-Net, Attention U-Net, R2-UNet, Attention R2-Unet, and U-Net3+). Also, in this research, an experiment was conducted to evaluate the influence of the choice of different bands of field images on the accuracy of the considered segmentation methods. The experiment showed that among all models, Attention R2U-Net (t2) proved to be more robust and reliable for different kinds of crops (accuracy 97.59–99.96%). The authors also evaluated the impact of using different combinations of image bands (such as RGB, RedEdge, NearIR, and NDVI) on the segmentation accuracy of the neural network model. The combination of RGB, NearIR, and NDVI channels allowed for the high values of all 8 metrics used in this research (0.41–1.77% more than the standard combination of RGB bands). The use of the RedEdge band has a significant negative impact on the quality of segmentation of the nitrogen level in the agricultural field. The proposed method based on Attention R2U-Net (t2) and a combination of RGB, NearIR, and NDVI bands is stable for different types of agricultural landscapes and can help to improve crop nutrition and yield. [ABSTRACT FROM AUTHOR]

Understanding spatial variability of soil properties is essential to support land management decisions. However, despite the growing worldwide emphasis on integrated landscape management, soil variations resulting from land use changes have rarely been documented. The study used the land health surveillance concept in combination with simple geostatistical approaches to describe key soil properties among land use types and characterize their spatial variability in the highlands of Cameroon. A total of 320 soil samples were collected in two sites with contrasting land uses (agricultural and pasture) and were analyzed for granulometric fraction, soil organic carbon (SOC), nitrogen (N), soil reaction (pH), phosphorous (P), calcium (Ca), potassium (K), magnesium (Mg), aluminum (Al) and zinc (Zn). The spatial correlations between the soil properties revealed the factors responsible for the observed differences and showed that wide ranges were obtained in agricultural site as opposed to pasture. SOC and N decreased in the order of forest > grassland > fallow > croplands > pasture due to inherent soil properties, anthropogenic activities, land cover/land use and topographic factors. Kriged maps provided detailed visualization of soil properties at landscape scale, and helped to identify critical areas for targeted land management interventions to improve land quality. The spatial distribution of selected soil properties showed a well-defined pattern of higher concentrations in the lowlands and valleys and areas with permanent vegetation cover in both sites. These results are useful for improving the efficient use of inputs such fertilizers. Context-specific land management based on spatial variability of soil properties is highly recommended and more research is required to generalize our knowledge about spatial variability of soil health indicators and the casual factors in the highlands of Cameroon. [ABSTRACT FROM AUTHOR]

In present study, geostatistical techniques were applied to assess the spatial variability of DTPA-extractable micronutrients which are named heavy metals as chemistry such as; iron (Fe), copper (Cu), manganese (Mn) and zinc (Zn) in the non-tillaged layer in Ordu province-Altınordu district, Black sea region, Eastern part of Turkey. Study area was approximately 40095.8 ha where was divided into grids with 2500 × 2500 m spacing with including 66 sampling points from 0-0.2 m in depth. Soil reaction (pH) was the least variable property while electrical conductivity (EC) was the most variable. While the highest nugget effect occurred for Ext-Cu with moderate spatial dependence, the lowest for Ext-Mn with strong spatial dependence. The greatest range of influence (17424 m) occurred for Ext-Cu and the least range (692 m) for Ext-Zn. [ABSTRACT FROM AUTHOR]

In present study, geostatistical techniques was applied to assess the spatial variability of exchangeable cations such as; calcium (Ex-Ca2+), magnesium (Ex-Mg2+), potassium (Ex-K+) and sodium (Ex-Na+) in the tillaged layer in a Perm State Agricultural Academy Farm site in Perm region, West Urals, Russia. A 250x100 m plot (approximately 2.35 ha) was divided into grids with 25x25 m spacing that included 51 sampling points from 0-0.2 m in depth. Soil reaction (pH) was the least variable property while the Ex-K was the most variable. The greatest range of influence (237.6 m) occurred for Ex-Ca and the least range (49.7 m) for Ex-Mg.

Precision agriculture (PA) has been used for ≥25 years to optimize inputs, maximize profit, and minimize negative environmental impacts. Legumes play an important role in cropping systems, by associating with rhizobia microbes that convert plant-unavailable atmospheric nitrogen into usable nitrogen through symbiotic nitrogen fixation (SNF). However, there can be field-level spatial variability for SNF activity, as well as underlying soil factors that influence SNF (e.g., macro/micronutrients, pH, and rhizobia). There is a need for PA tools that can diagnose spatial variability in SNF activity, as well as the relevant environmental factors that influence SNF. Little information is available in the literature concerning the potential of PA to diagnose/optimize SNF. Here, we critically analyze SNF/soil diagnostic methods that hold promise as PA tools in the short–medium term. We also review the challenges facing additional diagnostics currently used for research, and describe the innovations needed to move them forward as PA tools. Our analysis suggests that the nitrogen difference method, isotope methods, and proximal and remote sensing techniques hold promise for diagnosing field-level variability in SNF. With respect to soil diagnostics, soil sensors and remote sensing techniques for nitrogen, phosphorus, pH, and salinity have short–medium term potential to optimize legume SNF under field conditions.

In this work we propose the use of the topographic position index (TPI), which takes into account the local topography for a given neighborhood, to delineate management units (MU) for site-specific systems. This study was performed in the province of La Pampa, in central Argentina, an area with sandy soils where the main limiting condition for crops is soil moisture. Usually, multi-annual yield maps are used for the delineation of MU. However, those are strongly influenced by issues that could be related to un-calibrated data and previous agronomical practices. Thus, there was a need for a methodology based on stable and unbiased parameters. The methodology was developed for a representative 100 ha field. The average size and orientation of the topographic structures were characterized applying the autocorrelation function on the topographic data, which was then used to determine an optimum neighborhood size for the TPI. TPI performed better than the topographic map to characterize the variability of the field. The correlation between yield and TPI was higher ( r = 0.74) than that between yield and topography ( r = 0.54). The resulting management units were delineated using an unsupervised classification approach on the TPI maps. From the confusion matrices, the overall accuracy was higher for the TPI derived maps than for the topography derived maps (62% against 47%) when compared to a yield map used as reference. We estimate that this methodology could be used for operational applications, the only requirement being topographic data for a given field, since it is simple, the algorithms used are unbiased and it could be performed using free software. [ABSTRACT FROM AUTHOR]

Se estudió la variabilidad espacial de propiedades químicas y físicas en un Typic Udivitrands arenoso de la granja experimental Tesorito de la Universidad de Caldas, ubicado en la Cordillera Central Colombiana a 5º 01’47” N, 75º 26’ 03” W y 2280 msnm. El área experimental se localiza en un Piedemonte Coluvial, con un manejo antecedente y actual en pastos, frutales de clima frío moderado y hortalizas. Se realizó un muestreo sistemático en red con una distancia aproximada entre sitios de muestreo de 30 m en un área efectiva de 3,96 ha; para un total de 60 sitios. El análisis geoestadístico se llevó a cabo en tres etapas: 1) análisis exploratorio de los datos utilizando los software Statgraphics Plus v 2.0 y SPSS 9.0, 2) análisis estructural o semivariografía. 3) interpolación por el método Kriging puntual utilizando el paquete geoestadístico GS+ versión 5.3b. Los modelos esférico y exponencial fueron los de mejor ajuste a los modelos experimentales excepto para el DPM, que se ajustó a un modelo lineal (Nugget puro). El alcance para las variables analizadas se encuentra dentro de la distancia máxima ( 350 m) del área de muestreo considerada para el cálculo de los semivariogramas, excepto para el fósforo que presento un alcance de 479,4 metros. El alcance fluctuó entre 33,9 m para el Na y 308,5 m para el DPM. La variabilidad espacial de atributos del suelo en el predio experimental es alta, siendo mayor en propiedades químicas, igualmente, permitió visualizar la variabilidad espacial del deterioro estructural del suelo coincidiendo con la zona de mayor variabilidad química.A study of the spatial variability of the chemical and physical properties of a Sandy Typic Udivitrands was conducted in the Tesorito Experimental Farm of Universidad de Caldas, located in the Colombian Central Cordillera at 5º 01’47” N, 75º 26’ 03” W and 2280 m elevation. The experimental plot was located in a Coluvial Piedmont with previous and current soil use and management based on pastures, moderately cold weather fruits and horticultural crops. A systematic sampling of an approximately 30 m grid was carried out at 15 cm depths with an effective area of 3,96 ha for 60 sites. The geostatistical analysis was carried out in three stages: 1) exploratory analysis of the data using the STATGRAPHICS Plus v 2.0 and SPSS 9.0 software packages; 2) structural analysis or “semivariography”; 3) interpolation by punctual Kriging using the geostatistial package GS + version 5.3b. Spherical and exponential models best fit the theoretical models except for DPM (Weighted Mean of Water Stable Aggregates) which better fit a linear model (pure Nugget). Ranges for spatial dependence for the analyzed variables were within the maximum distance ( 350 m) of the sampling area considered for the calculation of the semivariograms, except for phosphorous that showed a range of 479,4 m. Range fluctuated between 33,9 m for Na and 308,5 m for DPM. Spatial variability of soil attributes of the experimental plot was high, being higher for chemical properties. Geostatistical analysis allowed visualization of the spatial variability of soil structural deterioration which coincided with the area of greatest chemical variability.

Metodologias de mapeamento de plantas daninhas em áreas agrícolas estão sendo utilizados a fim de gerar mapas de aplicação localizada de herbicidas, sendo que, este mapeamento vem sendo feito através de ferramentas da agricultura de precisão. O mapeamento das plantas daninhas gera então mapas de tratamentos de herbicidas que comandam pulverizadores capazes de realizar a aplicação localizada de herbicidas, aproveitando o comportamento contagioso inerente da comunidade das plantas daninhas, porém poucos experimentos relatam a eficiência desses métodos. Este experimento teve como objetivo comparar metodologias de mapeamento do capim-colonião (Panicum maximum), com base na avaliação visual durante e após a colheita da cultura de milho. Durante a colheita foi conduzido o monitoramento com avaliação visual, feito por uma pessoa devidamente treinada e pelo operador antecipadamente orientado. Após a colheita, a avaliação visual foi feita por amostragens, numa grade regular de 20 x 20 m. Foi observada uma subestimação de 6% da área infestada, com uma infestação de mais de 80% de cobertura pelo método de mapeamento durante a colheita, quando comparado com o caminhamento na grade regular após a colheita. Os dois métodos foram coincidentes em 45% da área marcada.Weed mapping methodologies are being tested in agricultural fields to generate maps of spotted application of herbicides by using precision farming tools. Weed mapping may generate herbicide treatment maps, allowing herbicide spray only where needed. However, few experiments have reported the efficiency of these methods. This research aimed to compare Panicum maximum mapping methodologies based on visual assessment during and after corn harvesting. Monitoring combined with visual assessment was performed during harvesting by a duly trained worker and a previously oriented operator. Visual assessment was done by samplings after harvesting in a regular 20 x 20 m grid. An underestimation of 6% of the infested area was observed, with infestation over 80%, by the mapping method during harvesting. The two methods were coincident in 45% of the area studied.

Acquiring multi-temporal spatial information on vegetation condition at scales appropriate for site-specific agricultural management is often complicated by the need for meticulous field measurements. Understanding spatial/temporal crop cover heterogeneity within irrigated croplands may support sustainable land use, specifically in areas affected by land degradation due to secondary soil salinization. This study demonstrates the use of multi-temporal, high spatial resolution (10 m) SPOT-4/5 image data in an integrated change vector analysis and spectral mixture analysis (CVA-SMA) procedure. This procedure was implemented with the principal objective of mapping sub-field vegetation cover dynamics in irrigated lowland areas within the lowerlands of the Amu Darya River. CVA intensity and direction were calculated separately for the periods of 1998-2006 and 2006-2010. Cumulative change intensity and the overall directional trend were also derived for the entire observation period of 1998-2010. Results show that most of the vector changes were observed between 1998 and 2006; persistent conditions were seen within the study region during the 2006-2010 period. A decreasing vegetation cover trend was identified within 38% of arable land. Areas of decreasing vegetation cover were located principally in the irrigation system periphery where deficient water supply and low soil quality lead to substandard crop development. During the 2006-2010 timeframe, degraded crop cover conditions persisted in 37% of arable land. Vegetation cover increased in 25% of the arable land where irrigation water supply was adequate. This high sub-field crop performance spatial heterogeneity clearly indicates that current land management practices are inefficient. Such information can provide the basis for implementing and adapting irrigation applications and salt leaching techniques to site-specific conditions and thereby make a significant contribution to sustainable regional land management. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ambient conditions and sensor–plant distances may affect active sensors’ performance. [•] Effects of changes in light and temperature on three active sensors were investigated. [•] Active sensors’ performance is hardly affected by changes in light intensity. [•] Distance between the sensor and the target surface needs to be considered. [•] Varying device temperature should be taken into account. [ABSTRACT FROM AUTHOR]

Abstract: During the last three decades there has been great concern about the impact of agriculture on the environment and its resources. Conventional agriculture is based on whole field and mostly empirical approaches to defining and applying agrochemical inputs, which poses certain limitations regarding the management of existing variability in agricultural land. In this paper, the design and application of a fuzzy decision support system, concerning site specific nitrogen fertilization, is described. The system uses an easy but efficient way of solving the nitrogen equation under agricultural conditions and is based on knowledge elicitation and fuzzy logic methodologies. More specifically, the system is composed of two parts; a knowledge base and an analytical modular part which simulates nitrogen balance. The analytical part is built in a four level structure which consists of eleven fuzzy systems. The evaluation of the system presupposes the availability of 14 state variables that can be easily collected and refer to characteristics of the soil, weather and farming practices. The incorporated knowledge and the formulation of fuzzy rules were based on interviews with experts and on annotating scientific and technical bibliographic resources. A sensitivity analysis of the developed system was carried out in order to evaluate its robustness against errors or uncertainty in the state variables and further to assess and highlight the important variables. The application of the system using a set of point data, drawn from cotton fields in central Greece and stored in a Geographical Information System, is described in brief and the results show considerable variability in the recommended amount of nitrogen fertilizer among the reference sites. [Copyright &y& Elsevier]

Measures of soil electrical conductivity (EC) and elevation are relatively inexpensive to collect and result in dense data sets which allow for mapping with limited interpolation. Conversely, soil fertility information is expensive to collect so that relatively few samples are taken and mapping requires extensive interpolation with large estimation errors, resulting in limited usefulness for site-specific applications in precision agriculture. Principal component (PC) analysis and cokriging can be applied to create meaningful field scale summaries of groups of attributes and to decrease the estimation error of maps of the summarized attributes. Deep (0–90 cm) and shallow (0–30 cm) EC, elevation, and soil fertility attributes were measured in fields under corn (Zea mays L.) and soybean (Glycine max L.) rotations, at two sites in Illinois (IL) and two sites in Missouri (MO). Soil fertility and topography attributes were summarized by PC analysis. The first topography PC (TopoPC1) contrasted flow accumulation against elevation and curvature, to describe the main topographic pattern of the fields. The first soil fertility PC (SoilPC1) consistently grouped together cation exchange capacity (CEC), Ca, Mg, and organic matter (OM). SoilPC1 was well correlated to soil EC for all sites and cokriging with EC had higher r2 in the crossvariogram models compared to ordinary kriging. The second and third soil fertility PCs (SoilPC2 and SoilPC3) were concerned with soil pH and P, and reflected historic land use patterns. Maps of SoilPC2 and SoilPC3 had little relationship to soil EC or topography and so could not be improved by cokriging. [ABSTRACT FROM AUTHOR]

Successfully delineating management zones that differ in crop productivity is an important component of site-specific management. We compared the effectiveness of the digitally scanned color aerial infrared photographs and digital Landsat Thematic Mapper (TM) data for delineating within-field zones. The zones delineated using normalized difference vegetation index (NDVI) from TM data explained 34% of field yield variance compared with 9% for that from digitally scanned color aerial infrared photograph data. The zones from NDVI using Landsat TM were better able to capture spatial differences in plant growth and relatively stable soil attribute of surface soil organic carbon. [ABSTRACT FROM PUBLISHER]