The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices.

Precision agriculture technology can transform farming related data into useful information, which may lead to more efficient usage of agricultural resources and increase sustainability. This paper compares precision agriculture technology with traditional practices in scheduling fungicide application so as to manage late blight disease in tomato production. The following three fungicide scheduling strategies were evaluated: a calendar-based strategy, the BlightPro Decision Support System based strategy (DSS-based strategy), and a strategy that does not involve fungicide application. The data from field trials and computer simulation experiments were used to construct distributions of the net return per acre for the calendar-based and the DSS-based strategies. These distributions were then compared using three standard approaches to ranking risky alternatives, namely: stochastic dominance, stochastic dominance with respect to a function, and stochastic efficiency with respect to a function. Assuming no yield differences between the calendar-based and the DSS-based strategies, the calendar-based strategy was preferred for highly late blight susceptible cultivars, and the DSS-based strategy was preferred for moderately susceptible and moderately resistant cultivars. Assuming no yield differences, the value of the BlightPro Decision Support System ranged from −$28 to $48 per acre. With the yield improvement for the DSS-based strategy included, the DSS-based strategy was preferred for the cultivars in all of the disease-resistance categories with the value ranging from $496 to $1714 per acre.

Utilizing cover crop treatments can have significant agronomic benefits for a farm enterprise. However, implementing this technology introduces additional costs. Data were obtained from a Central Indiana case farm to evaluate the relationship between applied nitrogen and corn yield, and how this relationship is impacted by introducing three different species of cover crops: annual ryegrass, cereal rye, and an oats and radish blend. The resulting information was then translated into a partial budget so that the effects on net returns could be analyzed using historical prices and stochastic modelling. The results showed that the impact on net returns per acre varied among cover crop species. The implementation of annual ryegrass resulted in a negative change to net returns. Conversely, implementing cereal rye or an oats and radish blend resulted in a positive change to net returns, with the largest net benefits accruing to the oats and radish blend.

The relative contributions of fluctuating cattle performance; interest rates; and feeder cattle, fed cattle, and feed grain prices to profit variability of cattle feeding were examined in this study. Closeout data from 6696 pens of steers placed on feed between January 1980 and May 1991 at two western Kansas custom feedyards were used to estimate the relative impacts of prices and animal performance on cattle feeding profits. Combined, fed and feeder cattle prices explained 70 to 80% of profit variability, depending on placement weight. Overall, cattle prices and feeding costs explained at least 85% of the variation in profitability. Animal performance explained 5 to 10% of profit variability.

This study compares the long-run net returns to land of conventional corn/soybean and corn/soybean/wheat crop rotations to that of an organic corn/soybean/wheat crop rotation. The net returns to land for the organic crop rotation were found to be approximately $68 and $74 per acre higher than those of the conventional corn/soybean and conventional corn/soybean/wheat crop rotations, respectively. Average net return estimates are sensitive to price, yield, and cost assumptions. Organic crop prices would have to drop more than 17.8 percent and organic crop yields would have to drop more than 16.8 percent before the conventional corn/soybean crop rotation was more profitable than the organic corn/soybean/wheat crop rotation. These percentage changes are relatively small compared to the historical relationships between organic and conventional crop prices and yields. A risk model was used to examine the trade-off between expected net returns and downside risk. Converting even a small proportion of acreage to an organic corn/soybean/wheat crop rotation improves net returns and reduces downside risk compared to only utilizing conventional crop rotations.

Farm managers possess a broad spectrum of capabilities, ranging from tech-savvy, strategically focused managers to those grappling with operational challenges. Our study examines survey data from 403 US commercial producers in order to identify subsets of producers who differ in terms of resilience, management practices, producer sentiment, and other farm characteristics. Utilizing various supervised and unsupervised machine learning techniques, we uncover key farm characteristics that capture the most pronounced variations in survey responses. We then cluster the dataset using these key variables, maximizing separation between clusters and minimizing differences within clusters using Ward’s hierarchical clustering. Fisher’s exact tests are implemented to determine the statistical significance of differences in farm characteristics across the constructed clusters. Results confirm that resilience to strategic risk, managerial ability, producer sentiment, technology adoption, and demographics all vary significantly among commercial farms. In particular, we observe a trade-off among farms in regard to operators’ management abilities and their farms’ resilience. Farms with the highest resilience levels tend to show slightly lower managerial abilities. Conversely, farms with the strongest managerial abilities exhibit somewhat poorer farm resilience. The third group of farms, which encompasses 49% of our sample, displays the lowest levels of farm resilience, precision agriculture technology adoption, and managerial abilities and has the weakest growth expectations.