CHINA'S AGRICULTURAL WATER SCARCITY : EFFECTS ON INTERNATIONAL MARKETS

Water shortages in important grain-producing regions of China may significantly affect China's agricultural production potential and international markets. This paper provides an overview of how water scarcity could affect China's agricultural production and trade. The paper identifies the areas where available water resources are most overexploited and the crops most vulnerable to reductions in irrigation. We present preliminary results from modeling a decline in irrigated land in water scarce areas in China and the effect this would have on China's production and trade. Wheat and cotton are most vulnerable to a decrease in irrigated area in water scarce regions, and production for these crops could fall by 7-10 percent under a severe cutback in irrigation. The effect this will have on international markets will depend largely on the openness of China's border to imports. In addition, we describe recent conservation policies and how these may affect crop production in China.Resource /Energy Economics and Policy,

Interactions between Resource Scarcity and Trade Policy: The Potential Effects of Water Scarcity on China’s Agricultural Economy under the Current TRQ Regime

International Relations/Trade, Resource /Energy Economics and Policy,

Determination of Disease Impacts on Sunflower Yield

Diseases that infect sunflower frequently occur in North Dakota, but the impact they have on yield is unclear. The objectives of this research are to 1) evaluate fungicide efficacy, application timing and yield impact of Phoma black stem of sunflower and 2) determine the impact of diseases on sunflower yield in North Dakota and Minnesota. Results of 14 fungicide trials conducted between 2017 and 2019 showed that yield losses to Phoma black stem were infrequent, but the disease could be managed by application of several available and efficacious fungicides applied at growth stage R1. Analysis of survey data collected over 11 years from 1,003 sunflower fields revealed that when diseases were determined to be a production-limiting factor, mean yield was 427 kg/ha less than in fields where no was production-limiting factor was reported. Results of these studies may help sunflower growers make decisions that optimize yield on their farms

China's Ongoing Agricultural Modernization: Challenges Remain After 30 Years of Reform

Thirty years ago, China began implementing a series of reforms to improve efficiency in agricultural production. These, and subsequent, reforms reshaped China’s position in the world economy. China’s rapid economic development and transformation from a planned to a market-oriented economy, however, has reached a stage where further efficiency gains in agricultural production will likely hinge on the development of modern market-supporting institutions. The development of market-supporting institutions in China will bring about long-term and sustainable benefits to producers and consumers in China and the global agricultural economy. This report provides an overview of current issues in China’s agricultural development, policy responses to these issues, and the effects of these policies on China’s growing role in international markets.China, economic reform, economic development, agricultural production, agricultural trade, Agricultural and Food Policy, International Relations/Trade, Production Economics,

Aggressive Behaviors in Dementia: Prevalence and Caregiver Reactions

Purpose The purpose of this dissertation study was to describe and explore the prevalence of aggressive behavior types (verbal aggression, destroying property, and threatening to hurt others) and their relationship with caregiver reactions (upset and confidence) among diverse, community-dwelling persons with dementia and their caregivers. Methods This study used both secondary data analyses and semi-structured interviews. Baseline data from 630 participants in the NIH Resources for Enhancing Alzheimer’s Caregiver Health II (REACH II) initiative was analyzed. Then, thirteen Black/African-American caregivers were purposively recruited and interviewed. Results More than a third (N = 241) of caregivers reported one or more aggressive behaviors in one week. Verbal aggression was most frequently reported (N = 217), then threatening to hurt others (N = 54), and destroying property (N = 45). Black/African-Americans were more than twice as likely as White/Caucasians to report threatening to hurt others, after controlling for covariates (AOR=2.26, p=0.035, 95% CI [1.06-4.84]). Over two-thirds of caregivers reported dichotomous upset with each behavior type. A statistically significant negative correlation with a medium strength of association between upset and confidence was found for total behaviors, verbal aggression, and threatening to hurt others. Caregivers differed statistically significantly by race/ethnicity in reporting upset with (p=0.003) and confidence managing (p=0.006) verbal aggression, as well as confidence managing behaviors overall (p=0.001). In interviews with Black/African-American caregivers, two overarching themes emerged: Care Challenges, including subthemes Taking care of a stranger, Hurtful interactions, Overcoming the past, and Social and financial strain and Success Strategies, including subthemes “It’s the disease….not the person,” “I got to do what I gotta do,” “We didn’t argue….we didn’t insist,” and “Don’t put her in a position to fail.” Conclusion Prevalence of aggressive behaviors in dementia was high, differing only slightly by race/ethnicity. Caregivers overall were challenged by aggressive behaviors in terms of their level of upset and confidence, differing somewhat by race/ethnicity. Themes emerging from interviews suggested caregivers used cognitive and behavioral strategies to address caregiving challenges. Cultural and contextual factors may be key to understanding why aggressive behaviors occur and to developing effective interventions to help families effectively prevent and manage them

POPULATION CODING IN LAMINAR CORTICAL CIRCUITS

One of the fundamental questions in neuroscience is to understand how encoding of sensory inputs is distributed across neuronal networks in cerebral cortex to influence sensory processing and behavioral performance. The fact that the structure of neuronal networks is organized according to cortical layers raises the possibility that sensory information could be processed differently in distinct layers. The goal of my thesis research is to understand how laminar circuits encode information in their population activity, how the properties of the population code adapt to changes in visual input, and how population coding influences behavioral performance. To this end, we performed a series of novel experiments to investigate how sensory information in the primary visual cortex (V1) emerges across laminar cortical circuits. First, it is commonly known that the amount of information encoded by cortical circuits depends critically on whether or not nearby neurons exhibit correlations. We examined correlated variability in V1 circuits from a laminar-specific perspective and observed that cells in the input layer, which have only local projections, encode incoming stimuli optimally by exhibiting low correlated variability. In contrast, output layers, which send projections to other cortical and subcortical areas, encode information suboptimally by exhibiting large correlations. These results argue that neuronal populations in different cortical layers play different roles in network computations. Secondly, a fundamental feature of cortical neurons is their ability to adapt to changes in incoming stimuli. Understanding how adaptation emerges across cortical layers to influence information processing is vital for understanding efficient sensory coding. We examined the effects of adaptation, on the time-scale of a visual fixation, on network synchronization across laminar circuits. Specific to the superficial layers, we observed an increase in gamma-band (30-80 Hz) synchronization after adaptation that was correlated with an improvement in neuronal orientation discrimination performance. Thus, synchronization enhances sensory coding to optimize network processing across laminar circuits. Finally, we tested the hypothesis that individual neurons and local populations synchronize their activity in real-time to communicate information about incoming stimuli, and that the degree of synchronization influences behavioral performance. These analyses assessed for the first time the relationship between changes in laminar cortical networks involved in stimulus processing and behavioral performance

Learning Style Preferences of Undergraduate Dietetics, Athletic Training, and Exercise Science Students

The study assessed the preferred learning style (LS) of college students and compared LS preferences among students majoring in Dietetics, Exercise Science, and Athletic Training. LS questionnaires were distributed to students (N=693, mean age 20.5±1.7) enrolled in health science courses at three Midwestern universities. Most students preferred a converger LS followed by assimilator, accommodator, and diverger. Some students preferred a combination of two LS. Chi square results indicated a significant relationship between college major and LS. Students in health majors were all observed to have a significant LS preference, namely the converger LS. However, distributions of preferred LS within each major differed. Understanding preferred LS of college students in different academic programs may increase the effectiveness of teaching and learning

Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study

This paper presents a machine learning technique for using large unlabelled survey datasets to aid automatic classification. We have demonstrated the benefit of this technique on a simulated synthetic aperture sonar (SAS) dataset. We designed a machine learning model to encode a representation of SAS images from which new SAS views can be generated. This novel task requires the model to learn the physics and content of SAS images without the requirement for human labels. This is called self-supervised learning. The pre-trained model can then be fine-tuned to perform classification on a small amount of labelled examples. This is called semi-supervised learning. By using a simulated dataset we can step-by-step increase the realism to identify the sources of difficulty for applying this method to real SAS data, and have a performance bench mark from this more idealised dataset. We have quantified the improved accuracy for the re-view model (ours), against a traditional self-supervised approach (autoencoder), and no pre-training. We have also demonstrated generating novel views to qualitatively inspect the model's learned representation. These results demonstrate our re-view self-supervised task aids the downstream classification task and model interpretability on simulated data, with immediate potential for application to real-world UXO monitoring