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Precision Agriculture (PA) manages field heterogeneities and enables informed site-specific management. While PA helps improve farming efficiency and profitability, challenges prior to and following PA adoption can prevent many farmers from widely using it. This paper aims to understand producers’ challenge perceptions using 1119 survey responses from U.S. Midwest farmers. The majority (59%) of respondents have adopted at least one PA technology, while the minority (14%) had not adopted any PA technologies. Cost (equipment and service fee), brand compatibility, and data privacy concerns topped other concerns from the average producer’s point of view. Among all producers, 60% regarded PA equipment and service fee as too high, followed by 50% who viewed brand compatibility and data privacy as their major concerns. Producers at more advanced adoption stage indicated reduced concerns in most categories. Yet, there were similar concerns towards data privacy issue regardless of the adoption status. Furthermore, brand compatibility issue is more of a concern for adopters than for non-adopters. Estimation results from partial proportional odds (PPO) models show that factors that frequently affect producers’ perceived challenges include adoption status, cropland acres, age, education, information sources, farming goals, soil characteristics, and region variables. Findings from this study can aid PA stakeholders in identifying target groups, tailoring future development, research, and outreach efforts, and ultimately promoting efficient PA usage on a broader scale. 

In many scientific experiments, multiarmed bandits are used as an adaptive data collection method. However, this adaptive process can lead to a dependence that renders many commonly used statistical inference methods invalid. An example of this is the sample mean, which is a natural estimator of the mean parameter but can be biased. This can cause test statistics based on this estimator to have an inflated type I error rate, and the resulting confidence intervals may have significantly lower coverage probabilities than their nominal values. To address this issue, we propose an alternative approach called randomized multiarm bandits (rMAB). This combines a randomization step with a chosen MAB algorithm, and by selecting the randomization probability appropriately, optimal regret can be achieved asymptotically. Numerical evidence shows that the bias of the sample mean based on the rMAB is much smaller than that of other methods. The test statistic and confidence interval produced by this method also perform much better than its competitors.

 

Precision Agriculture (PA) technologies are well known to be useful in addressing field heterogeneities and enabling informed site-specific management decisions. While profitability is the foremost factor considered by farmers when making PA adoption decisions, information in this regard is lacking from the farmers' perspective. This paper analyzed 1119 farmer responses from a 2021 survey conducted in four states along the western margins of the U.S. Midwest. Our findings show that while most (around 60%) non-adopters indicate that they are unaware of PA profit change, adopters are likely to rate a major profit increase. About two thirds of adopters rated at least a 5% increase in profitability towards variable rate (VR) fertilizer application (72%), VR seed application (68%), and automatic section control (66%). We modeled farmers' profit change subsequent to PA adoptions. Our regression results demonstrate that the profits from PA usage increase over time and that use of conservation practices likely influences PA profitability in a positive way. As soil quality and weather factors also affect profit ratings, it would be beneficial to compare and demonstrate profitability potential of various PA technologies on a regional basis and tailor the promotion efforts to farmers most likely to benefit from them. 

RNA macromolecules, like proteins, fold to assume shapes that are intimately connected to their broadly recognized biological functions; however, because of their high charge and dynamic nature, RNA structures are far more challenging to determine. We introduce an approach that exploits the high brilliance of x-ray free-electron laser sources to reveal the formation and ready identification of angstrom-scale features in structured and unstructured RNAs. Previously unrecognized structural signatures of RNA secondary and tertiary structures are identified through wide-angle solution scattering experiments. With millisecond time resolution, we observe an RNA fold from a dynamically varying single strand through a base-paired intermediate to assume a triple-helix conformation. While the backbone orchestrates the folding, the final structure is locked in by base stacking. This method may help to rapidly characterize and identify structural elements in nucleic acids in both equilibrium and time-resolved experiments.

 

Extreme weather events have cost lives and financial losses across the United States. Moreover, they are expected to increase in frequency, and this will exacerbate their impact on vulnerable sectors such as agriculture. But how farmers could adapt to extreme weather events by adopting different conservation practices has received slight attention in the literature. This study examines how farmers' perceptions of drought and flooding influence their decisions to implement conservation practices in their conventional crop fields. Out of the 350 farmer responses we received, fewer than half indicated a likelihood to adopt no-tillage/reduced tillage (43%), cover crops (40%), crop diversification (37%), and integrated crop-livestock grazing (29%). Using this data and a multivariate probit modeling framework, we show that farmers’ decisions can be partly explained by their perception of drought but not by their perception of flooding. Specifically, the perceived number of drought years significantly increases the likelihood of adopting no-tillage/reduced tillage and diversified cropping in the future. However, the number of drought years is not significantly associated with the use of cover crops and integrated crop-livestock grazing. These results suggest that the effects of extreme weather events on adoption of conservation practices as adaptive measures vary across different practices. Therefore, adaptation policies that make use of conservation practices must be tailored to farmers’ needs and priorities to be effective. 

While conservation practices promote soil health and reduce the negative environmental effects from agricultural production, their adoption rates are generally low. To facilitate farmer adoption, we carried out a survey to identify potential challenges faced by farmers regarding conservation tillage and cover crop adoption in the western margin of the US Corn Belt. We found farmers' top two concerns regarding conservation tillage were delayed planting, caused by slow soil warming in spring, and increased dependence on herbicide and fungicides. Narrow planting window and lack of time/labor were perceived by farmers as the two primary challenges for cover crop adoption. Some sense of place factors, including the commonly included dimensions of attachment, identity and dependence, played a role in farmers' perceived challenges. For example, respondents more economically dependent on farming perceived greater challenges. We found that farmers' challenge perceptions regarding reduced yield and lack of time/labor significantly decreased as years of usage increased, implying that time and experience could dilute some challenges faced by farmers. Our findings indicate that social network use, technical guidance and economic subsidies are likely to address the concerns of farmers and facilitate their adoption of conservation practices.

 

The use of intelligent decision support systems (DSS) in precision farming provides an opportunity to improve agricultural recommendations and reduce the impacts of agriculture on the environment. Despite the benefits offered by DDS, many farmers remain skeptical of using these hardware and software tools, and their adoption rates have remained low. A survey of 312 South Dakota farmers examined the barriers and opportunities for their engagement with DSS. Exploratory factor analysis was used to analyze 13 Likert scale survey items that probed farmers’ concerns about DSS. Factor loadings indicated that farmers’ concerns are related to high cost, insufficient knowledge, lack of confidence, and cyber security and privacy. A latent profile analysis (LPA) method was used to classify respondents into profiles or groups based on their dimensions of concerns (cost, knowledge, confidence, and security). Results of the LPA revealed that the sample of farmers could be grouped into four distinct profiles that ranged from low to high confidence in the use of DSS for agronomic decision‐making. Giving attention to farmer comfort/concern profiles allows for a more inclusive and better targeted engagement with farmers and potentially increase the adoption of PA. This knowledge can be vital for technology developers, policymakers, and extension services who are keen to promote PA usage among large‐, medium‐, and small‐scale farmers in the United States.