Precision Agriculture Practices Research Articles

Adoption and perspectives of precision farming in Denmark

Precision farming (PF) and site-specific input application based on GPS has been a management tool and option for arable farmers for about 10 years. About 400 Danish farmers have already adopted some PF practices on their farms. This adoption and the technical and economic perspectives of PF have been studied in two mail surveys, personal interviews and focus groups with farmers, advisers and experts. Farmers and stakeholders are in general optimistic about the future perspectives of these high-technology systems despite the difficulties in showing the economic and environmental gains. Lack of compatibility between different technical systems is however mentioned as a barrier for adoption.

Calibration Methodology for Mapping Within-field Crop Variability using Remote Sensing

A successful method of mapping within-field crop variability of shoot populations in wheat ( Triticum aestivum) and barley ( Hordeum vulgare L.) is demonstrated. The approach is extended to include a measure of green area index (GAI). These crop parameters and airborne remote sensing measures of the normalised difference vegetation index (NDVI) are shown to be linearly correlated. Measurements were made at key agronomic growth stages up to the period of anthesis and correlated using statistical linear regression based on a series of field calibration sites. Spatial averaging improves the estimation of the regression parameters and is best achieved by sub-sampling at each calibration site using three 0·25 m 2 quadrats. Using the NDVI image to target the location of calibration sites, eight sites are shown to be sufficient, but they must be representative of the range in NDVI present in the field, and have a representative spatial distribution. Sampling the NDVI range is achieved by stratifying the NDVI image and then randomly selecting within each of the strata; ensuring a good spatial distribution is determined by visual interpretation of the image. Similarly, a block of adjacent fields can be successfully calibrated to provide multiple maps of within-field variability in each field using only eight points per block representative of the NDVI range and constraining the sampling to one calibration site per field. Compared to using 30 or more calibration sites, restricting samples to eight does not affect the estimation of the regression parameters as long as the criteria for selection outlined in this paper is adhered to. In repeated tests, the technique provided regression results with a value for the coefficient of determination of 0·7 in over 85% of cases. At farm scale, the results indicate an 80–90% probability of producing a map of within crop field variability with an accuracy of 75–99%. This approach provides a rapid tool for providing accurate and valuable management information in near real-time to the grower for better management and for immediate adoption in precision farming practices, and for determining variable rates of nitrogen, fungicide or plant growth regulators.

Hierarchical data fusion for mapping soil units at field scale

We analyzed a highly complex soilscape of fluvial sediments by a hierarchical expert system. Using (i) inquiries, (ii) relief analysis on basis of a DEM 5, and (iii) soils' apparent electrical conductivity (EM38) as a database, we first defined zones of identical pedogenic context. Next, multi-temporal remote sensing data of winter wheat were obtained by an airborne multi-spectral scanner (Daedalus-ATM), which gives radiometric information with a geometric (ground) resolution of 1 m 2 (pixel size). Leaf area index (LAI) was semi-physically modeled using red and near-infrared canopy reflectances and related to above-ground biomass. Further, the resulting spatial patterns of vegetation parameters were processed by image analysis methods, i.e. an opening–closing procedure using a circular element with a radius of 5 m. These coarser patterns of LAI and biomass, respectively, were interpreted as patterns of site quality within each zone of pedogenic context. By our multi-temporal approach we were able to distinguish between stationary and time-variant pattern. Combined with point calibration on basis of a 50-m raster we identified available water capacity (AWC) and O 2 deficiency due to stagnant water as the most important soil properties constituting site quality for plant growth. Our results will be used for precision agriculture practices in future.

On Estimation and Prediction for Spatial Generalized Linear Mixed Models

We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. We develop a Monte Carlo version of the EM gradient algorithm for maximum likelihood estimation of model parameters. A by-product of this approach is that it also produces the MMSE estimates for the realized random effects at the sampled sites. This method is illustrated through a simulation study and is also applied to a real data set on plant root diseases to obtain a map of disease severity that can facilitate the practice of precision agriculture.

Spatial Variability of Soil Properties and Weed Populations in the Mississippi Delta

Simulation models and precision agriculture practices may require more detail and certainty about soil spatial variability than provided by soil surveys. This study described soil and weed spatial variability in 50‐ha subareas of two sites included in the Mississippi Delta Management Systems Evaluation Areas project. Objectives were (i) to describe the spatial variability of soil properties and (ii) to determine relationships between spatially variable weed populations and soil properties. Surface soil samples were collected at nodes of 60‐m square grids prior to planting cotton (Gossypium hirsutum L.) in 1996. Field‐moist soil was analyzed for microbial activity. Air‐dried soil was used to determine soil organic C, pH, and texture. Fluometuron and either clomazone, metolachlor, or norflurazon were banded over the crop row at planting. Weed counts were taken 6 wk after herbicide application. The spatial variability of soil properties and weed populations was described using geostatistics. Soil microbiological activity exhibited limited spatial dependence, but pH, organic C, and texture semivariograms were well‐described with spherical models. Although short‐range (<60 m) variability was often high, the range of spatial dependence typically exceeded 120 m. Total weeds were spatially dependent both years; however, weeds susceptible to control by herbicide were not. Weed densities were significantly greater (P < 0.05) in areas that had higher organic C and finer texture. Areas of low organic C and coarse soil often had no weeds. Thus, more uniform weed control might be achieved by varying preemergence herbicide application rate. Acceptable weed control might be achieved with lower herbicide application rates in certain areas.

Modeling spatial variation in productivity due to tillage and water erosion

The advent of precision farming practices has heightened interest in managing field variability to optimize profitability. The large variation in yields across many producer fields demonstrated by yield–monitor–equipped combines has generated concern about management-induced causes of spatial variation in soil productivity. Soil translocation from erosion processes may result in variation in soil properties across field landscape positions that produce long-term changes in soil productivity. The objective of this study was to examine the relationships between soil redistribution caused by tillage and water erosion and the resulting spatial variability of soil productivity in a soil catena in eastern South Dakota. An empirical model developed to estimate tillage erosion was used to evaluate changes expected in the soil profile over a 50-year period on a typical toposequence found in eastern South Dakota and western Minnesota. Changes in the soil profile due to water erosion over a 50-year period were evaluated using the WEPP hillslope model. The tillage erosion model and the WEPP hillslope model were run concurrently for a 50-year period to evaluate the combined effect of the two processes. The resulting changes in soil properties of the root zone were evaluated for changes in productivity using a productivity index model. Tillage erosion resulted in soil loss in the shoulder position, while soil loss from water erosion occurred primarily in the mid to lower backslope position. The decline in soil productivity was greater when both processes were combined compared to either process acting alone. Water erosion contributed to nearly all the decline in soil productivity in the backslope position when both tillage and water erosion processes were combined. The net effect of soil translocation from the combined effects of tillage and water erosion is an increase in spatial variability of crop yields and a likely decline in overall soil productivity.

Laboratory evaluation of a proximal sensing technique for simultaneous measurement of soil clay and water content

Precision agriculture, largely the application of information and communications' technologies to in-field data gathering and management, may be regarded as `best practice' for crop growth in the future because of its twin goals of maximising economic returns whilst concurrently minimising environmental impact. The practice of precision agriculture, whether it be to differentially apply fertiliser, seed, pesticide, irrigation or tillage requires detailed knowledge of the spatial and temporal variation of crop yield components, weeds, soil-borne pests and attributes of physical, chemical and biological soil fertility. However, a detailed description of fine or even coarse scale variation in soil properties has always been difficult and costly to perform. Sensing and scanning technologies are currently being developed to more efficiently and economically describe and obtain precise information on the extent and variability of soil attributes which affect crop growth and yield. Combining these technologies with vastly improved ground positioning systems allows detailed mapping of soil resource and crop yield variability which may therefore be an important input for site-specific decision making. Experiments were conducted to design an invasive sensor for real-time, simultaneous measurements of clay, organic matter and soil water content from reflectance of a suitable wavelength or combination of wavelengths in the near infra-red (NIR) portion of the electromagnetic spectrum. Soil materials were prepared with varying amounts of clay, soil water and organic matter according to a response–surface design, and the reflectance spectra measured at 2-nm intervals from 1300 nm to 2500 nm. Response–surface models were fitted to the reflectance data at specified wavelengths. Reflectance showed significant response to clay content and soil water but not to organic matter. A thorough selection procedure using non-linear modelling and root-mean-square-error of prediction was used to derive the four most suitable wavelengths (1600, 1800, 2000 and 2100 nm) for simultaneously measuring clay and soil water content. In a simulation experiment clay content was more accurately predicted than water content.

Spatial Scale Requirements for Precision Farming: A Case Study in the Southeastern USA

AbstractPrecision farming has created a critical need for spatial data on crop yield and related soil characteristics. However, because data are not without cost, users need practical guidelines for spatial resolution on which to collect soil and plant data. Our objectives were (i) to describe variation observed in crop response in the southeastern Coastal Plain of the USA, (ii) to compare it with variation in other regions, and (iii) to offer suggestions for precision farming practices in the southeastern Coastal Plain. From 1985 to 1995, corn (Zea mays L.), wheat (Triticum aestivum L.), soybean [Glycine max (L.) Merr.], and grain sorghum [Sorghum bicolor (L.) Moench] yields were mapped at from 10‐ to 20‐m resolution in an 8‐ha field at Florence, SC. Also available were topography (30‐m resolution), depth to clay (15 m), and in 1993, plant height on one date (9 m), canopy temperature on four dates (1.5 m), and detailed crop and soil information at selected sites. Yield of all crops in all years was significantly (P < 0.0007), though not strongly (median r2 = 0.3), correlated with soil map unit. In 1993, infrared thermometer canopy temperature minus air temperature (∆Tc) was correlated with soil map unit, even on the second day after a 46‐mm rain. Spherical semivariograms fitted to yields had ranges from 57 to 252 m (median = 79 m) and nugget/sill ratios from 0.00 to 0.56 (median = 0.32). Semivariograms for canopy temperature and plant height had ranges from 43 to 77 m. If the spatial structure for common soil characteristics matches the spatial structure for crop response, Coastal Plain soils may require study at finer resolution than the >100‐m grid that is commonly used in precision farming.

The future of serving agriculture with weather/climate information and forecasting: some indications and observations

Weather/climate information and forecasting services to agriculture are discussed from three perspectives: tradition, transition and technology. Traditional weather and climate services to agriculture have been highly successful. For example, numerous applied climatological studies and references have been derived from the primarily voluntary network of climate observers in the United States. However, traditional climatic studies will probably not meet the needs of the future, given the significant transitions and technologies affecting agriculture. Agriculture is going through a number of significant transitions which will dictate to some degree what future services will be provided by the agrometeorological community. These transitions should be considered in the testing and deployment of new technologies. Both profitability and sustainability principles are fueling a largely voluntary move to the implementation of ‘precision farming’ information, methodologies and technologies. Concurrently, officials at all levels of government are considering regulatory legislation which would mandate that agricultural producers use more precise and well-documented management practices. To a large degree both the implementation of precision farming practices, as well as the enforcement of new regulatory laws to ensure food safety, and the quality of the environment will depend upon an even greater utilization of weather/climate information and forecasts. Though new technologies will be providing improvements in forecasting and improved climate services are becoming available, a number of important issues remain unclear and must be addressed by a coordinated effort within the agricultural weather and climate community, starting first with a renewed effort to educate agricultural clientele about the potential new services which the discipline has to offer.