In Southwestern Ontario (Canada), the availability of broadband, or high-speed internet, likely influences the adoption of precision agriculture (PA) technologies and functions of these technologies which enable real-time data sharing between the field and the digital cloud, and back again to the farm-level user. This paper examines the reasons why PA technologies are, or are not adopted, and adoption in relation to varying levels of broadband access. Broadband access is defined here with variables such as availability at download/upload speeds and types of connection. We report the results of an online survey which collected information from field crop producers in Southwest Ontario as of November 2016 on their use of PA technologies. The survey data were cross-referenced to information on broadband within the region. Findings indicate that adoption of PA technologies among crop farmers in SW Ontario varies according to machine, product and service. Internet access (defined by the national standard of the time at five megabits per second (Mbps) download and one Mbps upload) was available on 37% of the farm premises surveyed. Over two-thirds of farmers surveyed lack sufficient symmetrical connectivity anticipated by integrated and cloud-based applications in precision agriculture. Only seven percent of respondents have access to fibre. Those users with access to high-speed internet are all adopters of at least one PA technology identified in this study. Age, farm size and income are relevant factors influencing PA adoption. It does not follow, however, that access to broadband (≥5 Mbps) is required for the adoption of (at least one) PA technology. There is an apparent gap between internet access and use of data generated by PA. Use of wireless-enabled data transfer from PA applications, was found to be low, due to two considerations: a) bandwidth and b) data security. There is an expectation, according to the opinion data collected in the e-survey that the internet is an important factor in future PA adoption and increasingly so for agricultural business.

Yield maps and GPS-based soil maps have been increasingly used in U.S. agriculture but little research has explored the economic relationship between mapping technologies and agricultural productivity. Research on this relationship is lacking, perhaps because maps are information inputs that do not directly enter the production function in a comparable way to conventional inputs. A stochastic frontier model was used to evaluate one potential avenue through which mapping technologies may influence productivity – technical efficiency. After controlling for farmers’ potentially endogenous choice of map technologies, adoption of yield maps has a positive influence on technical efficiency, and unexpectedly, adoption of soil maps has a negative influence on technical efficiency. Given that yield maps are a basic information input, this suggests that increased availability of some information or data-type inputs, by themselves, can have indirect production benefits to farmers.

After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or ‘business-as-usual’ system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36-ha field in central Missouri during 1993 to 2003. Following this, a ‘precision agriculture system’ (PAS) with conservation practices was implemented for the next 11 years to address production, profit, and environmental concerns. The PAS was dynamic and included no-till, cover crops, growing winter wheat (Triticum aestivum L.) instead of corn in a section of the field where corn was often not profitable, site-specific N for wheat and corn using canopy reflectance sensing, variable-rate or zonal P, K and lime using intensively grid-sampled data, and targeting of herbicides based on weed pressure. Differences in yield and yield variability between the two systems were recently evaluated, but profitability comparisons have not been made. Results indicated that PAS maintained profits in the majority (97%) of the field without subsidies for cover crops or payments for enhanced environmental protection. Profit or net returns were only lower with PAS in the drainage channel where no-till sometimes hindered soybean stands and wet soils caused wheat disease. Although profit gains were not realized after 11 years of PA and conservation practices, results indicate this type of system can maintain profits. Furthermore, this information should help growers gain confidence that PA and conservation practices will be successful.

Profitability analyses of site-specific nitrogen management strategies have often failed to provide reasons for adoption of precision farming implements. However, often effects of precision farming on product quality and price premiums were not taken into account. This study aims to evaluate comparative advantages of site-specific nitrogen management over uniform nitrogen management with respect to aspects of risk, considering fertilizer effects on grain quality and price premiums. We developed a model field with two subfields representing different yield classes to investigate how consideration of grain quality affects the economic potential of site-specific nitrogen management and to what extent site-specific nitrogen management can have a risk-reducing effect compared to uniform nitrogen management. Results show that higher crop yields as well as higher protein contents of the grains can be achieved with site-specific nitrogen management compared to uniform nitrogen management. Higher grain quality and associated higher product prices result in higher economic benefit. Furthermore, a risk-reducing effect can be expected with site-specific nitrogen management by maintaining a certain grain quality with a higher probability.

Recent development in precision agriculture technologies have generated massive amount of geospatial data of farming, such as yield mapping, seeding rates, input applications, and so on. However, producers are still struggling to convert those precision data into farm management decisions to improve productivity and profitability of farming.  Indeed, deriving accurate decisions at each site of the field requires complex and comprehensive modeling of crop yield responses to various inputs (fertilizer, water, seeds, etc.) that are very complicated and varying across growing conditions (soil, weather, slope, etc.).  Even the most state-of-the-art crop growth simulation models still have difficulty to reach that accuracy level, and can easily generate large margin of errors in some parts of the field.  While the accurate modeling of crop growth is still an ongoing research effort in plant and soil sciences, this study explores an alternative decision-making method from the economic perspective.  The main idea is a simple profit mapping approach that constructs high resolution spatially explicit profit maps for the crop fields and stops planting the unprofitable areas within the fields.  As a case study, 21 corn-soybeans fields’ geospatial production data were collected from a farm in the Mississippi Delta from 2012 to 2016.  Profitability maps are calculated at resolution of 10-meter grids by computing the crop sale revenue and direct costs of farming operation for each grid. Based on the assumed price scenario that is similar to the current market (corn $3.5/bushel, and soybean $10/bushel), about 4% of the total 88,023 grids are unprofitable over the period 2012 to 2016.  The total amount of profit loss from those grids is $6,896 annually, which can be avoided by retiring those grids from production.  When crop prices decline, the profit gain from retiring unprofitable grids will further increase.  This study provides an illustration of a simple yet useful approach to convert digital farming data into decision making, and quantifies the profit improvement that can be achieved at whole farm level.  

In the current state of digital agriculture, many digital technologies and services are offered to assist North American soybean producers.  Opportunities for capturing and analyzing information related to soybean production methods are made available through the adoption of these technologies.  However, often it is difficult for producers to know which digital tools and services are available to them or understand the value they can provide.  The objective of this study was to assimilate and categorize current digital technologies available to producers today, understand how they are being used, and potential value they can provide.  

Over 100 currently available digital technologies were sorted into six categories that included: Data Warehousing, Production Benchmarking, Production Analysis, In-Season Monitoring, Crop Modeling, and Recommendations. Categorizing these technologies provided; 1) a clearer understanding of technology implementation, 2) an alignment of a digital technology to a producer’s intended use if adopted, and 3) insight to producers considering these digital technologies. 

A producer survey was also administered along with interviews of agriculture technology experts, and results were then summarized and analyzed.  Survey results indicated that 70% of producers are confident that their data is valuable and 91% are actively using technology in soybean production.  Many survey responses indicated concerns about the potential added value, data management logistics, and disconnects between technology providers and end users.  Results of this study provide insight for soybean producers on the implementation and use of current and future digital technologies.  

The agreement signed at COP-21 reaffirms the vital compromise of Brazil with sugarcane and ethanol production. To meet the established targets, the ethanol production should be 54 billion liters in 2030. From the agronomic standpoint, two alternatives are possible; increase the planted area and/or agricultural yield. The present study aimed to evaluate the economic and environmental impacts in sugarcane production meeting the established targets in São Paulo state. In this context, were evaluated how the precision agriculture (PA) technologies could help sugarcane production reach the established targets from agreement. Only expanding the area, an increase of 2 million hectares (ha) would be required, suggesting that the expansion should be in other Brazilian states as well. However, scientific results have shown that it is more feasible to increase the agricultural yield. This alternative can reduce 29% of the total production costs. Considering that the average yield should rise from 77.5 Mg ha-1 to 111 Mg ha-1 by 2030, the total production costs will be reduced 29%. The use of PA technologies can contribute significative reducing production cost and increasing agricultural yield. From an environmental point of view, the adoption of PA technologies can reduce around 20% of climate change and fossil depletion compared with current scenario, that will be essential to reach the goals. Manage spatial and temporal variability of crops and soils will allow to maximize the sugarcane production, reducing production costs and environmental impacts through the rational use of inputs.