<p>Farmers in the digital age require accurate, relevant farm-level data to make sound management decisions for their operations. Mobile applications, or apps, are emerging as a valuable management and decision-making tool for farm operators, but are still in their infancy as a technological innovation. Farmer adoption and use of mobile apps has received relatively little attention in the scholarly literature compared to more established farm management tools and communication media. The researcher examined Indiana farmers’ use and perceptions of mobile apps as tools for management and decision-making. A theoretical perspective was developed from the Diffusion of Innovation Theory and the Technology Acceptance Model to guide the investigation. Data on attitudes, behaviors, and demographic characteristics were collected through interviews with 55 Indiana farmers in late 2018 and early 2019. Quantitative interview items were analyzed through descriptive statistics while open-ended items were coded for emergent themes.</p> <p>Study participants reported a median age of 41 years and an average of 26 years farming. Nearly all study participants (98.2%) considered mobile applications useful to farm operations. A smaller but significant majority (76.4%) of participants rated mobile apps as easy to use. In terms of content, the most common use of apps among study participants was for general purpose utilities such as banking and messaging, followed by weather and agriculture-related apps such as Granular and FieldNet. Ease of use and content of application were among attributes considered most important by study participants when considering adoption of new apps. About three-fourths (76.4%) of the study participants indicated intentions to adopt additional mobile applications in the future. </p> <p>A series of items addressed study participants’ awareness of open source technology. About three-fourths (72%) indicated not previously having heard of the terminology. When asked to share their thoughts on the term open source, a large majority (84%) of participants provided vague or seemingly unrelated responses ranging from cloud-related, to the capability of apps to exchange information, to software being open to all users. </p> <p>As part of the analysis, the researcher categorized study participants into one of three adopter categories – early adopters, early majority, or late majority – based on the length of time participants reported using mobile apps, attitudes toward the technology, and intention to adopt apps in the future. Cross-tabulation analysis revealed that early adopters of mobile app technology did not differ significantly at the .05 level from later adopters in terms of age, years farming, or size of operation.</p> <p>Finally, an empirical test was conducted to assess utility of the Technology Acceptance Model for conceptualizing behavioral intent to adopt mobile agricultural applications. As expected from theory, correlational analysis revealed positive and moderately strong relationships (p < .05) between perceived usefulness and attitude toward mobile applications, and between perceived ease of use and attitude toward mobile applications. The relationship between attitude and behavioral intention to adopt additional mobile applications was statistically non-significant at the .05 level, contrary to theory. The importance of exploring alternative theoretical perspectives in future research is discussed.</p> <p>Results from this research contribute to the growing literature on how farmers assess and use mobile applications as farm management and decision-making tools. Findings have implications for application developers, as well as those involved in education and marketing of mobile agricultural applications. </p>

<div>The use of genotypic markers in plant breeding has greatly increased in the last few decades. In this dissertation, I report on three topics that illustrate how genotypic marker information can be applied in maize breeding to increase genetic gain. In the first chapter¹, I describe how genotypic and phenotypic data can be used to predict the mean, variance, and superior progeny mean of virtual biparental populations. I use these predictions to identify optimal breeding crosses out of a commercially relevant collection of North American dent inbreds. In the second chapter, within the context of early generation maize inbred development, and using a hybrid testcross data set, I report on the change in genomic prediction accuracy as the size of the training set increases and compare the accuracy of different genomic selection models. In the third chapter², I used a multi-variable linear regression approach known as genomewide association (GWA) analysis to identify particular genetic locations, known as quantitative trait loci (QTL), that are associated with maize in orescence traits.</div>

Corn production in the United States has become increasingly efficient over the years. The use of nitrogen fertilizers has played a substantial role in this efficiency. Nitrogen drives biomass production which leads to increased yields. Unlike other nutrients, nitrogen is more mobile making it easier to lose through leaching and volatilization. The first part of this analysis uses an econometric model to examine the relationship between nitrogen usage and weather data. This relationship leads to farm management decisions to reduce nitrogen fertilization expenses. In addition to the use of nitrogen fertilizers, farmers in Kentucky take advantage of an abundance of poultry litter as a fertilizer source. Traditional poultry litter fertilization methods are being challenged by new technology, sub-surface injection, which has the potential to increase corn yields as compared to other methods. The second part of this analysis uses a resource allocation linear programming model to determine the economic viability of the sub-surface injection method for both spring and fall fertilizer applications. This model also reveals both farm management implications and provides valuable information for the development and commercialization of the sub-surface injector.

The purpose of this research is to provide quantitative and limited qualitative analysis for the United States Intermountain West on the economic effectiveness of risk management alternatives in an agricultural operation. This research is not meant to be an exhaustive comparison of every potential combination of alternatives and risk scenarios. Instead, specific parameters such as farm size, crops grown, risk attitudes and risk management strategies are set to guide the research and offer a basis of comparison. This research evaluates several levels of coverage using Multiple Peril Crop Insurance, Crop Revenue Coverage Insurance, Adjusted Gross Revenue - Lite Insurance and a limited interaction of the futures market. Microsoft Excel and the add-in Simetar was used to perform the quantitative analysis. A set of spreadsheets were created to allow a variety of data to be easily input and manipulated. The values used in this research were based on the 2002 Census of Agriculture to create a "typical" farm considered in Box Elder County, Utah. The results generated were sorted and ranked according to four decision criteria in relation to the net income observed in each simulated scenario. These include: the probability that net income will exceed $0; a maxi-min; a maxi-max; and the maximum positive net income at a probability of occurrence of 0.5, resembling a Safety-First criterion. The later three decision criterion used correspond to risk attitudes that may be possessed by a producer: risk adverse, risk preferring or seeking, and risk neutral respectively. The quantitatively "best" observed results were then qualitatively compared to the next "best" result. In general the conclusion is made that some strategy is better than no strategy and the "best" risk management strategy is one compatible with the risk attitude of the producer and the parameters of the farm in consideration. There is no single strategy for all decision criterions that consistently outperforms all other strategies considered in this research.

"November 2002." Bibliography: leaves 73-80. Identifies main sources of farming risk in the Upper Eyre Peninsula as climate variability and financial risk. Finds that farmers manage risk by having high equity and off-farm investment, using gross margin analysis, having farm management deposits, diversifying varieties, minimizing tillage, relying on experts for grain marketing and keeping stock for high price periods.

A study was conducted to determine (1) the N contribution of lentil (<i>Lens culinaris</i> Medikus) and its effect on subsequent wheat (<i>Triticum aestivum</i> L.) crops in the Dark Brown and Brown soil zones of Saskatchewan, (2) if synchronization of available N contributes to the rotation benefit, and (3) N fertilizer replacement value of the N effect of lentil on the subsequent wheat crop. Two approaches were used: a landscape-scale study established at Dinsmore in 1994, and a repeated small-plot study established at several locations (Dinsmore, 1993 and 1994; Clavet, Conquest, Eston and Zealandia, 1995). In the landscape-scale study, N2 fixation by lentil ranged from 28 to 46 kg ha-1, with an average of 34 kg ha -1 N and an N balance of -36 kg ha-1. In the small-plot study, N2 fixation ranged from 10 to 112 kg ha -1 with an average of 59 kg ha-1 N and an N balance of -22 kg ha-1. In the landscape-scale study, preseeding available N on the rotation that incorporated lentil was 29 and 61% higher than in the monocropped wheat in the first and second subsequent crops, respectively. The corresponding values for the small-plot study were 59 and 14%, respectively. N availability was well synchronized with the period of maximum N demand by the wheat crop in both rotations. However, both lentil-residue N and wheat-residue N were poorly synchronized with the period of maximum N demand by the wheat. The rotation effect was exhibited in higher grain yield (23% higher in the landscape-scale study, and 21% higher in the small-plot study). Approximately 10 and 70% of the rotation benefit in the landscape-scale and small plot studies, respectively, were attributed to the N effect. Nitrogen fertilizer replacement value ranged from 47 to 89 kg ha-1 N. Therefore, lentil can fix enough N and indirectly contribute to greater available N in subsequent crops than in crops following non-legumes. However, the actual amount of N derived from lentil residue is small. The greater available N is attributed to spared N and enhanced soil N mineralization. Hence, under the conditions of this study, enhancement of available N contributed more to the rotation benefit than synchronization. The high NFRV show that inclusion of lentil in the rotation can enhance available N in the cropping system and potentially reduce fertilizer N application.

No description available.

No description available.

No description available.

No description available.