Economics of robots and automation in field crop production

Precision Agriculture - This study reviewed research published after 1990 on the economics of agricultural mechatronic automation and robotics, and identified research gaps. A systematic search was...     

Impact of variable-rate application of nitrogen on yield and profit: a case study from South Africa

The response of maize (Zea mays) to banded variable-rate nitrogen (N) application over a period of 3 years (2002/3–2004/5) is analyzed. The experimental design alternated variable-rate (VR) and single-rate (SR) applications of N. The yield monitor data were spatially autocorrelated and therefore were analyzed with spatial regression methods. The baseline spatial regression model defined in this study showed that the VR treatment, treatment by year and treatment by management zone were statistically significant. Sensitivity tests were applied; the first showed that VR treatment had a yield advantage when soil depth was greater than the field average of 174 cm. The second test showed that the VR N rates applied were close to those that would maximize profit. Partial budgeting indicates that benefits from VR vary from year to year, but in this test VR was slightly more profitable than uniform rate application. Economic sensitivity testing indicates that farm size and the price of maize are the key factors in the profitability of VR N.     

Report from the conference, ‘identifying obstacles to applying big data in agriculture’

Precision Agriculture - Data-centric technology has not undergone widespread adoption in production agriculture but could address global needs for food security and farm profitability. Participants...     

Spatial analysis of yield monitor data: case studies of on-farm trials and farm management decision making

A 3-year case study was undertaken of how North American farmers use yield monitors for on-farm trials in farm management decision making. Case study methods were used because relatively few farmers quantitatively analyze yield monitor data. At this early research stage, insufficient farm management information about the data was available to ask the right questions in a large-scale survey. In addition to the formal case study of farmers experienced at using yield monitors to collect on-farm trial data, the study evaluated the effect of yield monitor data quality on farm decisions. Two levels of yield data quality included standard output where the default settings of farm-level mapping software were accepted and where filtering of the data was undertaken. Results indicated that yield data quality affects farm management decisions. In addition, farmers receiving a spatial analysis of their on-farm trial data tended to use split-field designs instead of replicated split-planter designs. They were also more confident in their decisions than before participation in the spatial analysis project, and made decisions more quickly.     

Modeling local terrain attributes in landscape-scale site-specific data using spatially lagged independent variable via cross regression

Precision Agriculture - Analysis methods for landscape-scale site-specific agricultural datasets have been adapted from a wide range of quantitative disciplines. Due to spatial effects expected at...     

Economics of autonomous equipment for arable farms

By collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.