A model to analyze as-applied reports from variable rate applications

During variable rate operations, controller systems report information (as-applied files) about desired rates and real applied rates on georeferenced points along the machine tracks. These reports are useful for operation quality control but they have not been widely used to their potential. The goal of this study was to create a model to help analyzing as-applied files based on quantifying and locating off-rate errors and their probable related sources. The model calculates off-rate error at every point and classifies them as less than target rate, acceptable or over the target rate. Possible error sources are classified regarding three aspects: vehicle path position (inward, middle or outward), high rate change (step up or down) and vehicle acceleration or deceleration. A pulled type applicator (application 1) and a self-propelled applicator (application 2) were analyzed. An average of 30.6 % of the recorded points was considered application errors (10 % off the target rate). 70.5 % of them occurred on high rate change points on application 1 and 69.7 % on acceleration/deceleration points on application 2. The self-propelled applicator performed better during high transition rate than the pulled type which performed poorly when transition rate exceeded 10 %. The model determined the major and minor factors related to application error. It provided means to assess equipment limitations and its impact over the quality of application. The trials demonstrated its flexibility and how it can improve the use of as-applied files.     

Spatial variability of soil properties and yield of a grazed alfalfa pasture in Brazil

Knowledge of the spatial variability of soil properties and of forage yield is needed for informed use of soil inputs such as variable rate technology (VRT) for lime and fertilizers. The objective of this research was to map and evaluate the spatial variability of soil properties, yield, lime and fertilizer needs and economic return of an alfalfa pasture. The study was conducted in a 5.3 ha irrigated alfalfa pasture in Sao Carlos, SP, Brazil that was directly grazed and intensively managed in a 270-paddock rotational system. Alfalfa shoot dry matter yield was evaluated before grazing. Soil samples were collected at 0–0.2 m depth, and each sample represented a group of 2 or 3 paddocks. Apparent soil electrical conductivity (ECa) was measured with a contact sensor. The cost of producing 1 ha of alfalfa was estimated from the amount of lime and fertilizer needed and was then used to estimate the total cost of production for the dairy system. The alfalfa dry matter yield was used to simulate the pasture stocking rate, milk yield, gross revenue and net profit. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms with VESPER software, the soil fertility information and economic return were modeled with SPRING software. The results showed that geostatistics and GIS were effective tools for revealing soil and pasture spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application maps. Spatial variation in forage and spatial estimates of stocking and milk yield are adequate pasture management tools. Spatial analyses of needs, forage availability and economic return are management tools for avoiding economic problems, as well as potential environmental problems, caused by unbalanced nutrient supplies and over- or under-grazing.     

Variable rate nitrogen fertilizer response in wheat using remote sensing

Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha^?1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro^® and RapidEye?, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N₂O emissions, nitrate leaching, and farmer’s profit.     

Spatially variable insecticide applications for early season control of cotton insect pests

Our research has shown that cotton insect pests, specifically tarnished plant bugs, Lygus lineolaris (Palisot de Beauvois) (Heteroptera: Miridae) can be controlled early season in commercial cotton fields in Mississippi, USA, using spatially variable insecticide applications. Technology was developed for using GIS-based map scouting and a technique called the line-intercept method for obtaining low-level insect population counts in both rapidly growing areas of cotton and poorer growing areas. Using these population characteristics in combination with heuristic knowledge of the cotton fields and with the GIS maps, a spatially sensitive map could then be developed that could drive a spatially variable insecticide application for the control of the insect pest. We outline the steps needed to develop an automated technology for overcoming the time-sensitive events for early season control of cotton pests. This technology not only includes software systems for processing multispectral images to spatially variable insecticide application maps for spray controllers in the field but also high-speed wireless local area network (WLAN) technology for automated delivery of these controller application maps and for acquisition of as-applied and harvest maps from the field.     

Adaptation of regional digital soil mapping for precision agriculture

In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m² spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.     

Variable rate spraying application on cotton using an electronic flow controller

Vegetation indices (VI) obtained by optical sensors have a positive correlation with various attributes of cotton plant growth. This work is aimed at evaluating the variable rate application of plant growth regulator (PGR) and fruit ripener on zones defined by VI and penological measurements using a sprayer equipped with a relatively low cost electronic flow controller on the height, percentage of open fruits, yield and net income. The work was done in a 92 ha field during crop seasons 2012/2013 and 2013/2014, and in a 202 ha field, during the crop season 2014/2015. Two spray applications were made using variable rate technology (VRT) of the PGR and one fruit ripener, in both harvest seasons, according to three VI classes formed by a previous mapping. The uniformity of the cotton height and opened fruits contribute to a similar yield across zones. Uniform plant height facilitates cotton harvest. The ripener helps to ensure all the cotton is ready to be harvested at the same time. In this trial, use of VRT technique to manage the PGR and fruit ripener application increased net income by US$152.28 ha^?1, but this estimate is based on yields that are not statistically significantly different from the control. This research confirms that PGR and fruit ripener can be sufficiently managed with an electronic flow controller to result in more uniform cotton plant height and yields within fields, but it leaves open the question of whether VRT PGR is profitable even with the lower cost electronic flow controller.     

Estimating soil organic matter using interpolation methods with a electromagnetic induction sensor and topographic parameters: a case study in a humid region

Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (EC_a), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The EC_a measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg^?1, whereas EC_a varied from 6.7 to 14.7 mS m^?1. These two variables were significantly correlated (r = ?0.6, p < 0.05); hence, EC_a was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and EC_a. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.     

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.     

Data fusion techniques for delineation of site-specific management zones in a field in UK

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.     

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395–1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band–band r ², principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R_740/720) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r ² = 0.551, P < 0.000).     

Sampling of subterranean termites <Emphasis Type="Italic">Syntermes</Emphasis> spp. (Isoptera: Termitidae) in a eucalyptus plantation using point process and geostatistics

The Syntermes genus, the most significant termite pest in eucalyptus cultivation, damages roots and debarks plant rings. This can kill the seedlings of this plant, and thus require replanting. Integrated management, based on sampling plans can reduce damage to eucalyptus seedlings and allows the rational use of chemical control. The objectives were to model the spatial distribution of the Syntermes spp. foraging holes using the Matérn-cluster point process in the Cerrado region (Brazilian savannah), simulate a sampling plan for termite hole density, produce maps of foraging hole densities using geostatistics and validate the simulated sampling plan in the field. The distribution of the Syntermes spp. foraging holes was spatially non-homogeneous and it adjusted to the Matérn-cluster point process model in the Minas Gerais Cerrado areas. The best Syntermes sampling plan simulation in the area of Cerrado is to launch a circular 5 m radius parcel every 100 m (sampling error <5 %) in a systematic manner. The approach of point processes, combined with geostatistics, is adequate to produce maps for the termite Syntermes spp. infestation in the eucalyptus plantation.     

Outlier identification of soil phosphorus and its implication for spatial structure modeling

Outliers are classified as global outliers and spatial outliers. Up to now, there is little information about the outliers especially the spatial outliers and their influence on the spatial structure modeling of soil properties. A total of 537 soil samples were collected based on a 30 × 30 m grid in a permanent dairy farm in southeast Ireland. Graphic methods of histogram and box plot combined with Moran’s I were applied to detect the outliers of soil phosphorus (P). Sixteen outliers (5 global outliers and 11 spatial outliers) of soil P were found in the study area. Compared to the raw data, the data with global outliers excluded always had the larger global Moran’s I value indicating a stronger spatial autocorrelation. Clear spatial clusters (High–High and Low–Low clusters) were observed based on local Moran’s I. The High–High spatial clusters were located around the main farm yard and near the traffic route due to more intensive management by farmers. The Low–Low spatial clusters were mainly close to the river. For these areas, P fertilizer or slurry should be applied for healthy grass growth. The dataset with outliers excluded had a reliable semi-variogram model with a low nugget/sill ratio (32.4 %), which was closed to its corresponding transformed data (30.5 %). The cross-validation results revealed that the dataset without outliers had the strongest linear regression model (r = 0.768), indicating that the outliers played an important role in the spatial structure modeling.     

Mapping wheat nitrogen uptake from RapidEye vegetation indices

Mapping wheat nitrogen (N) uptake at 5 m spatial resolution could provide growers with new insights regarding nitrogen-use efficiency at the field scale. This study explored the use of spectral information from high resolution (5 × 5 m) RapidEye satellite data at peak leaf area index (LAI) to estimate end-of-season cumulative N uptake of wheat (Triticum spp.) in a heterogeneous, rainfed system. The primary objectives were to evaluate the usefulness of simple, widely used vegetation indices (VIs) from RapidEye as a tool to map crop N uptake over three growing seasons, farms and growing conditions, and to examine the usefulness of remotely sensed N uptake maps for precision agriculture applications. Data on harvested wheat N was collected at twelve plots over three seasons at four farms in the Palouse region of Northern Idaho and Eastern Washington. Seventeen commonly used spectral VIs were computed for images collected during ‘peak greenness’ (maximum LAI) to determine which VIs would be most appropriate for estimating wheat N uptake at harvest. The normalized difference red-edge index was the top performing VI, explaining 81 % of the variance in wheat N uptake with a regression slope of 1.06 and RMSE of 15.94 kg/ha. Model performance was strong across all farms over all three seasons regardless of crop variety, allowing the creation of high accuracy wheat N uptake maps. In conclusion, for this particular agro-ecosystem, mid-season VIs that incorporate the use of the NIR and red-edge bands are generally better predictors of end-of-season crop N uptake than VIs that do not include these bands, thereby further enabling their use in precision agriculture applications.     

Monitoring of soil organic carbon over 10 years in a Mediterranean silvo-pastoral system: potential evaluation for differential management

Soil organic carbon (SOC) plays a vital role in determining the susceptibility to land degradation. The recommended procedure for the recovery of the characteristic poor soils of the Southern region of Portugal is the installation of grazed permanent pastures and increase of soil fertility. The objectives of this study were: (i) to identify the spatial and temporal patterns of soil nutrients at four points in time over a 10-year period in a perennial pastureland; (ii) to test new tools for survey of the spatial variability of soil nutrients; (iii) to evaluate the potential for differential organic management. A 6 ha permanent bio-diverse pasture field, grazed by sheep and improved by annual application of super phosphate fertilizer, was installed on a shallow soil in Mediterranean conditions. Spatial variability and temporal stability of topsoil macronutrients (phosphorus, nitrogen and potassium), SOC and pH were measured. The results indicate that SOC and pH have great potential for implementing differential management. In the case of SOC, the management classes map shows that over 80 % of the area has temporal stability, while more than 50 % of the area has low levels of SOC (<10 g kg^?1), justifying the potential for differential application of C-rich organic soil amendments. The geospatial measurements of apparent soil electrical conductivity (EC_a) and NDVI index showed significant correlation between these parameters and soil properties, revealing the potential of these tools for producing detailed soil maps, decisive for understanding the changes in soil properties under sustainable management systems.     

Sensing soil and foliar phosphorus fluorescence in <Emphasis Type="Italic">Zea mays</Emphasis> in response to large phosphorus additions

Additions of large loads of phosphorus (P) enriched animal manure to soils and the persistence of their environmental impact have been associated with continued water quality impairment in regions of high density of confined animal feeding operations. Foliar P in corn (Zea mays L.) and changes in labile P in Aquic Hapludults were determined following P application of 0–560 kg P ha^?1 as KH₂PO₄ and an application of Fe³⁺ (150 mg Fe³⁺ kg^?1) in field mini-lysimeters to develop calibrations of soil and plant nutritional responses. X-ray fluorescence (XRF) scanning of uppermost leaves of plants at the V2, V5, and V8 stages showed that foliar P proportionally increased with addition rates. Exchangeable and enzyme-labile P forms were effective indicators of foliar XRFS-P for up to 30 days after emergence. Phosphorus calibration curves developed for flag leaves showed that spatial distribution of foliar P (3.6, 4.2, and 5.3 g kg^?1) corresponded to field zones treated with 0, 15, and 30 kg P ha^?1 as dairy manure P for the past 18 years. Up-to-date crop uptake and availability of P in these Hapludults were best described by a square root function of soil XRFS-P and total exchangeable inorganic P (r² = 0.4; RMSE = 419 and 422 g ha^?1, respectively). Therefore, a timely knowledge of canopy P status and its linkage to actual soil P status supports in situ element-specific sensing and precision nutrient management in order to manage the declining use-efficiency in crops and reduce potential loss to the environment.     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).