Geography Theses and Dissertations
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Browsing Geography Theses and Dissertations by Subject "agriculture"
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Item Effectively evaluating environmental, social, and economic outcomes in voluntary sustainability programs: Lessons from Laos(2022) Traldi, Rebecca; Silva, Julie A; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Voluntary sustainability programs (VSPs) are a subset of environmental interventions which rely on participants’ willingness to engage, rather than mandatory regulation. VSPs have been a central component of sustainable development and environmental mitigation strategies for decades, with significant investments from nongovernmental organizations (NGOs), multilaterals, and the private sector. VSPs typically aim to positively influence environmental, economic, and social outcomes, although program-specific priorities often result in an uneven focus across these three domains (also known as the three pillars of sustainability). Despite their popularity, questions regarding the value of VSPs remain unanswered. Assessments of VSPs typically do not eliminate rival explanations for program outcomes when evaluating their successes and failures, thus limiting our understanding of their effectiveness.This dissertation addresses this gap by investigating socioeconomic and environmental outcomes for agriculture and forestry VSPs. Mixed methods including systematic review, inverse probability-of-treatment weighted regression (IPWR), and inequality and polarization decomposition provide insights both at a global level, and for two national case studies in Lao People’s Democratic Republic (hereafter Laos). A wide range of datasets inform the analysis, including nationally representative poverty and expenditure surveys and land-use land cover estimates derived from remotely sensed imagery. By exploring a variety of VSPs – including agricultural and forestry voluntary sustainability standards and sustainable development projects – the study acknowledges the context-specific nature of VSP impact, while also drawing generalizable insights relevant for different types of interventions. The research findings presented in this dissertation elucidate the degree to which VSPs deliver on stated goals and objectives. First, a systematic literature review reveals that the evidence base for VSP impact remains limited, with some geographies, sustainability outcomes, and project types receiving more inquiry and evaluation than others. Second, an IPWR analysis suggests that agriculture and forestry VSPs have achieved some success in generating positive outcomes – specifically, for poverty and forest cover. However, variations in project focus and design bring different results. For example, food security and livelihoods programs which prioritize local socioeconomic well-being can generate significant co-benefits for environmental outcomes, and resource management projects can positively impact forest cover. Conversely, the forest management projects considered here do not achieve significant benefits for poverty or forest cover – presumably due to challenges like land tenure insecurity, insufficient participant incentives, and persistent drivers of deforestation (illegal logging, large-scale concessions). Finally, an assessment of economic inequality and polarization associated with the Laos rubber boom demonstrates the importance of assessing how VSPs influence economic inequality. It also indicates that VSPs must address inequality’s systemic drivers – including dispossession from land and forest resources, lacking worker protections, livelihood vulnerability, and barriers for smallholders – to maximize potential benefits. Overall, this dissertation research provides an example of how evidence synthesis, quasi-experimental methods, and consideration of economic, social, and environmental sustainability can deepen our understanding of VSPs.Item Factors Influencing Remote Sensing Measurements of Winter Cover Crops(2016) Prabhakara, Kusuma; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. In addition to remote sensing imagery, advances have been made in the use of proximal sensors integrated with GPS for on-field measurements, and the comparability of such measurements between platforms, as well as based on processing level is important. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012-2013 winter growing season. There was a strong relationship between the Normalized Difference Vegetation Index (NDVI) and percent groundcover (r2 =0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The Triangular Vegetation Index (TVI) was most accurate in estimating high ranges of biomass (r2=0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). Accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops. Surface reflectance measurements were more correlated with proximal sensors compared to top of atmosphere, with intercepts closer to zero, regression slopes nearer to the 1 to 1 line, and less variance between measured values. NDVI was highly correlated with percent vegetative groundcover, though surface reflectance products did not necessarily improve the relationships. When the Scattering for Arbitrarily Inclined Leaves (SAIL) model was used with measured field variables reflective of realistic winter cover crop scenarios, there were not large differences between NDVI despite differences in residue cover and moisture. At low LAI, NDVI is not capable of differentiating between residue and vegetative cover.Item A GENERALIZED APPROACH TO WHEAT YIELD FORECASTING USING EARTH OBSERVATIONS: DATA CONSIDERATIONS, APPLICATION, AND RELEVANCE.(2012) Becker-Reshef, Inbal; Justice, Christopher C; Vermote, Eric; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study.