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. 2000. Monitoring cattle behavior and pasture use with GPS and GIS. Can. J. Anim. Sci. 80: 405-413. Precision agriculture is already being used commercially to improve variability management in row crop agriculture. In the same way, understanding how spatial and temporal variability of animal, forage, soil and landscape features affect grazing behavior and forage utilization provides potential to modify pasture management, improve efficiency of utilization, and maximize profits. Recent advances in global positioning system (GPS) technology have allowed the development of lightweight GPS collar receivers suitable for monitoring animal position at 5-min intervals. The GPS data can be imported into a geographic information system (GIS) to assess animal behavior characteristics and pasture utilization. This paper describes application and use of GPS technology on intensively managed beef cattle, and implications for livestock behavior and management research on pasture. Global positioning system monitoring can provide researchers with efficient and accurate information on grazing behavior. Previous research focused on tracking animals using data gathered by observation. Recent advances in GPS technology have allowed the development of lightweight collar receivers suitable for monitoring animal position at 5-min intervals. Data can be imported into a GIS to assess animal behavior characteristics and pasture utilization. Precision animal location recording allows researchers to evaluate pasture utilization, animal performance, and behavior. Researchers may assess the merits of pasture or paddock shapes and sizes, fence designs, grazing systems, forage composition and availability, location of shade, water, and supplements, and other variables that affect beef cattle operations. The objectives of this article are: 1) to review previous tracking technology; 2) to explain GPS animal monitoring; 3) to describe GPS tracking collar application for beef cattle; and 4) to discuss pasture livestock behavior and management research implications.

The selection of a spatial interpolation methods will impact the quality of site‐specific soil fertility maps. The objective of this study was to describe and predict the relative performance of inverse distance weighted (IDW) and ordinary kriging. Soil samples were collected on 30.5‐m grids for fields in five Kentucky counties and analyzed for pH, buffer pH, P, K, Ca, and Mg. From these data sets, 61‐m grid subsets were extracted. Data were interpolated with IDW and kriging procedures. Prediction efficiency (PE) was determined using an independent dataset (PEvalidation) and with cross‐validation (PEcross‐validation). Multiple stepwise regression was used to develop models that described the relative performance of ordinary kriging and IDW with statistical properties of the data. At the 30.5‐m grid scale, the performance of ordinary kriging relative to IDW improved as the range of spatial correlation increased and fit of the semivariogram model improved. However, at the 61.0‐m grid scale, the performance of ordinary kriging relative to IDW diminished as the degree of spatial structure increased and the fit of the semivariogram model improved. Alone, PEcross‐validation poorly describes the performance of PEvalidation across locations, soil properties, and sampling intervals (r2 = 0.18). However, in combination with the range of spatial correlation, substantial variability at the 30.5‐m grid scale was described for variables with sample semivariograms that reached plateaus (R2 = 0.61). In some situations, better decisions will be made regarding the use of these methods by considering the range of spatial correlation and cross‐validation statistics.

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Remote sensing (RS) technologies provide a diagnostic tool that can serve as an early warning system, allowing the agricultural community to intervene early on to counter potential problems before they spread widely and negatively impact crop productivity. With the recent advancements in sensor technologies, data management and data analytics, currently, several RS options are available to the agricultural community. However, the agricultural sector is yet to implement RS technologies fully due to knowledge gaps on their sufficiency, appropriateness and techno-economic feasibilities. This study reviewed the literature between 2000 to 2019 that focused on the application of RS technologies in production agriculture, ranging from field preparation, planting, and in-season applications to harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. We found an increasing trend in the use of RS technologies in agricultural production over the past 20 years, with a sharp increase in applications of unmanned aerial systems (UASs) after 2015. The largest number of scientific papers related to UASs originated from Europe (34%), followed by the United States (20%) and China (11%). Most of the prior RS studies have focused on soil moisture and in-season crop health monitoring, and less in areas such as soil compaction, subsurface drainage, and crop grain quality monitoring. In summary, the literature highlighted that RS technologies can be used to support site-specific management decisions at various stages of crop production, helping to optimize crop production while addressing environmental quality, profitability, and sustainability.