Precision farming technologies are now widely applied within Australian cropping systems. However, the use of spatial monitoring technologies to investigate livestock and pasture interactions in mixed farming systems remains largely unexplored. Spatio-temporal patterns of grain yield and pasture biomass production were monitored over a four-year period on two Australian mixed farms, one in the south-west of Western Australia and the other in south-east Australia. A production stability index was calculated for two paddocks on each farm. An example is given here for one paddock from Western Australia. The stability index described here is unique in that it combines spatial and temporal variation across both cropping and pasture phases. Co-efficient of variation in yield was used as the threshold value for determining stability. Production in each stability zone was analysed statistically for consistency and correlation between the cropping and pasture phases. Results indicate that the stability index can be used in mixed farming systems to assist in management decisions and for the paddock described, spatial and temporal variation in production between crop and pasture phases was strongly correlated.

Profitability in crop production is largely driven by crop yield, production costs and commodity prices. The objective of this study was to quantify the often substantial yet somewhat illusive impact of weather, management, and soil spatial variability on within-field profitability in corn and soybean crop production using profitability indices for profit (net return) and return-on-investment (ROI) to produce estimates. We analyzed yield and cropping system data provided by 42 farmers within Central and North Eastern Iowa from 2007 to 2014. The dataset was comprised of 380 site years from 77 fields. Commercial software was used to calculate spatial net return (profit) in crop production, ROI, and standard deviation in profit over time for individual fields. Iowa State University Estimated Costs of Crop Production in Iowa were used to calculate profitability maps. These profitability metrics were then joined with soil attributes (organic matter, drainage, slope), site-specific rainfall, crop rotation and environmental modeling for soil conditioning index and soil loss by erosion. The relationship between profitability metrics and site-specific field and within-field factors was analyzed for two Iowa Landform Regions: the Des Moines Lobe and the Iowan Surface. Within both Landform Regions, 10 to 50% of within-field areas had economic losses, especially during 2013 and 2014. We found a higher frequency of economic loss in poorly drained pothole vs. upland areas within the Des Moines Lobe.  With each additional cm of May or June rainfall, median field-level profits were reduced by $50 to $120 ha^-1for fields planted to corn.  Compared with corn, profitability of soybean fields was unaffected by May rainfall and less affected by June rainfall. The effect of rainfall in Eastern Iowa was different than in Central Iowa, with above normal July rainfall tending to increase profitability by $43 ha^-1with each additional unit of rainfall. Other than soil drainage, we did not find a significant effect of spatial factors on within-field profitability, indicating the predominance of rainfall and cropping systems. The presented analyses are critical for guiding design and development of future studies that can lead to the creation of risk mitigation tools for farmers.

Targeting management practices and inputs with precision agriculture has high potential to meet some of the grand challenges of sustainability in the coming century, including simultaneously improving crop yields and reducing environmental impacts. Although the potential is high, few studies have documented long-term effects of precision agriculture on crop production and environmental quality. More specifically, long-term impacts of precision conservation practices such as cover crops, no-tillage, diversified crop rotations, and precision nutrient management on field-scale crop production across landscapes are not well understood. To better understand these impacts, a 36-ha field in central Missouri was monitored for over a decade as both a conventional (1991-2003) and a precision agriculture system (PAS) (2004-2014). Conventional management was annual mulch-tillage in a 2 yr corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation. Key aspects of the PAS were the addition of no-tillage, cover crops, winter wheat (Triticum aestivum L.) instead of corn on areas with shallow topsoil and low corn profitability, and variable-rate nutrient (N, P, K, and lime) applications. The objective of this research was to evaluate how over a decade of PAS influenced temporal and spatial dynamics of grain yield. In the northern half of the field, wheat in PAS had higher relative grain yield and reduced temporal yield variation on shallow topsoil, but reduced relative grain yield on deep soil in the drainage channel compared to pre-PAS corn. In the southern half of the field where corn remained in production, PAS did not increase yield, but did reduce temporal yield variability. Across the whole field, soybean yield and temporal yield variation were only marginally influenced by PAS. Spatial yield variation of any crop was not altered by PAS. Therefore, the greatest production advantage of a decade of precision agriculture was reduced temporal yield variation, which leads to greater yield stability and resilience to changing climate.

This research evaluated the profitability and N use efficiency of real time on-the-go optical sensing measurements (OPM) and variable-rate technologies (VRT) to manage spatial variability in cotton production in the Mississippi River Basin states of Louisiana, Mississippi, Missouri, and Tennessee. Two forms of OPM and VRT and the existing farmer practice (FP) were used to determine N fertilizer rates applied to cotton on farm fields in the four states. Changes in yields and N rates due to OPM and VRT were not enough to produce higher net returns and improve N use efficiency relative to the FP.

Precision agriculture (PA) technologies used for identifying and managing within-field variability are not widely used despite decades of advancement. Technological innovations in agronomic tools, such as canopy reflectance or electrical conductivity sensors, have created opportunities to achieve a greater understanding of within-field variability. However, many are hesitant to adopt PA because uncertainty exists about field-specific performance or the potential return on investment. These concerns could be better addressed by understanding where variability in soil physical and chemical properties may have the greatest effect on crop responses to inputs, such as nitrogen fertilizer. Therefore, identifying fields that exhibit the most variation in soil characteristics (e.g. clay and organic matter content) and developing an indicator of variation that has the potential to affect crop responses to inputs could greatly advance PA adoption and use. The objectives of this research were to: 1) quantify the amount of potential soil variability over a large region, 2) generate an index that numerically identified fields that exhibit degrees of field variability, and 3) evaluate spatial clustering of variability over the region. This analysis focused on soil variability in agricultural fields across Missouri, USA. We calculated a variability index (VI) for clay and organic matter content at two depth increments (0-30 and 0-120 cm) using soil information from the National Resources Conservation Service’s (NRCS) Soil Survey Geographic database (SSURGO). Ranges in VI for clay at the two depth increments were 1-82 and 1-91 with an average of 2.4 and 2.2, respectively. Organic matter VI averaged 2.0 and 2.3 for the two increments with narrower ranges from 1-42 and 1-29, accordingly. Significant high clay VI clusters at both increments were observed mostly along the Missouri River floodplain and across southeastern Missouri along the Mississippi River. High organic matter VI clusters exhibited similar distributions along the Missouri and Mississippi River floodplains; however, significant clusters of low organic matter VI values occurred within the Central Claypan and Southern Mississippi River Alluvium major land resource areas. Output from this research could be used as a decision support tool to aide suppliers and practitioners in determining the greatest opportunities to implement PA.

Since its inception and acceptance as a benchmarking tool within the economics literature, data envelopment analysis (DEA) has been used primarily as a means of calculating and ranking whole-farm entities marked as decision making units (DMU) against one another.  Within this study, instead of ranking the entire farm operation against similar peers that encompass the study, individual data points from within the field are evaluated to analyze the site-specific technical efficiencies estimated at sub-field locations. A hypothetical grid superimposed upon a field creates the DMU’s so that scale efficiency can be visually assessed in a map and spatially analyzed. Input variables include as-applied inputs, geospatial data on soil characteristics, and aerial remotely-sensed imagery. Output variables were based upon yield monitor sensors from harvest equipment from one or more years and therefore one or more crops grown in rotation. Both bio-physical agronomic relationships and economic characteristics were evaluated. Analysis can be conducted on either physical units or on the dollar values of these inputs and outputs. The data here are analyzed by superimposing a grid over a production field in Kansas.  Once technical efficiencies were calculated for each site-specific grid cell, the results were spatially mapped across the field to form what looks quite similar to a yield map, only instead of yield, the map now represents the site-specific technical efficiency of that particular field.  From this point, tests for global and local spatial autocorrelation indicated the presence of spatial effects, further providing true economic insights into the variability generated either by nature or by the farmer.

These results are useful for the agricultural industry as they represent the first new techniques evaluating efficiency and economics applied to precision agriculture in many years. This initial study can easily be extended to include a farmer’s field with a deliberate intervention, i.e. on-farm experiment; where the technical efficiency of the experiment and in particular regression residuals can be assessed. Additional extensions to this technique can be applied to a community of farmers’ fields in a big data analysis.