College of Behavioral & Social Sciences
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The collections in this community comprise faculty research works, as well as graduate theses and dissertations..
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Item Russian Winter Cropland Mapping and Impact on Land Use(2024) Abys, Christian Joseph; Skakun, Sergii Dr.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation provides an in-depth analysis of the transformation in Russia’s winter wheat industry over the past two decades, focusing on production growth, land use changes, and advancements in monitoring techniques. The study reveals a substantial 149% increase in wheat production and a 35% rise in farmland area from 2000 to 2020, driven predominantly by winter wheat, which now represents a significant portion of global exports. Despite this growth, there is notable yearly volatility in production, with USDA Foreign Agriculture Service forecasts exhibiting considerable uncertainty, particularly in area estimations which has substantial impacts on the global wheat export market. To address these challenges, the research utilizes long-term MODIS satellite data to analyze cropland expansion and intensification in southwestern Russia, identifying a 29% increase in winter wheat cropland with distinct patterns of expansion and intensification across different latitudes. The study highlights the ongoing capacity for further cropland intensification. Furthermore, this research introduces Sentinel-1 SAR imagery as an effective solution to the issue of cloud coverage, which hampers optical data accuracy. By employing various machine learning models, including multi-layer perceptron, long short-term memory, and random forest, the study demonstrates that Sentinel-1 SAR enhances the accuracy of in-season cropland mapping. The results show that Sentinel-1 SAR data reduces uncertainty in area estimations by two-thirds compared to MODIS data, offering improved monitoring capabilities. Collectively, this research provides valuable insights into Russia’s agricultural dynamics, addresses key uncertainties in forecasting, and proposes advanced methodologies for more accurate and reliable agricultural assessmentsItem A SYNTHETIC APERTURE RADAR (SAR)-BASED GENERALIZED APPROACH FOR SUNFLOWER MAPPING AND AREA ESTIMATION(2023) KHAN, MOHAMMAD ABDUL QADIR; Skakun, Sergii; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The effectiveness of remote sensing-based supervised classification models in crop type mapping and area estimation is contingent upon the availability of sufficient and high-quality calibration or training data. The current challenge lies in the absence of field-level crop labels, impeding the advancement of training supervised classification models. To address the needs of operational crop monitoring there is a pressing demand for the development of generalized classification models applicable for various agricultural areas and across different years, even in the absence of calibration data. This dissertation aims to explore the potential of the C-band Sentinel-1 Synthetic Aperture Radar (SAR) capabilities for building generalized crop type models with a specific focus on identifying and monitoring sunflower crop in Eastern Europe. Globally, the sunflower ranks as the fourth most important oilseed crop and stands out as the most profitable and economically significant oilseed crop. It is extensively cultivated for the production of vegetable oil, biodiesel, and animal feed with Ukraine and Russia as the largest producer and exporter in the world. In the first step, this study explores the interaction of Sentinel-1 (S1) SAR signal with sunflower to identify and monitor phenological stages of sunflower. The analysis examines SAR backscattering coefficients and polarizations in Vertical-Horizontal (VH), Vertical-Vertical (VV) and VH/VV ratio, highlighting differences between ascending and descending orbits due to sunflower directional behavior caused by heliotropism. Based on the unique SAR-based signature of sunflower the study introduces a generalized model for sunflower identification and mapping which is applicable across time and space. It was observed that the model based on features acquired from S1-based descending orbits outperforms the one based on ascending orbit because of the sunflower’s directional behavior: user’s accuracy (UA) of 96%, producer’s accuracy (PA) of 97% and F-score of 97% (descending) compared to UA of 90%, PA of 89% and F-score of 90% (ascending). This model was generalized and validated for selected sites in Ukraine, France, Hungary, Russia and USA. When the model is generalized to other years and regions it yields an F-score of > 77% for all cases, with F-score being the highest (>91%) for Mykolaiv region in Ukraine. The generalized approach to map sunflower was applied to assess the impact of the Russian full-scale invasion of Ukraine on national sunflower planted areas. The sunflower planted areas and corresponding changes in 2021 and 2022 were estimated using a sample-based approach for area estimation. Sunflower area was estimated at 7.10±0.45 million hectares (Mha) in 2021 which was further reduced to 6.75±0.45 Mha in 2022 representing a 5% decrease. The findings suggest spatial shifts in sunflower cultivation after the Russian invasion from southern/south-eastern Ukraine under Russian controlled to south-central region under Ukrainian control. The first objective of this dissertation highlights the difference of ascending and descending S1 orbits for sunflower monitoring due to its directional behavior, an aspect not fully researched and documented previously. The implemented generalized approach based on sunflower phenology proves to be an accurate and space-time generalized classifier, reducing time, cost and resources for operational sunflower mapping for large areas. Also, the disparity between sample-based area estimates and SAR-based mapped areas caused due to speckle were substantially reduced emphasizing the role of S1/SAR in global food security monitoring.Item Landsat-based operational wheat area estimation model for Punjab, Pakistan(2018) Khan, Ahmad; Hansen, Matthew C.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wheat in Punjab province of Pakistan is grown during the Rabi (winter) season within a heterogeneous smallholder agricultural system subject to a range of pressures including water scarcity, climate change and variability, and management practices. Punjab is the breadbasket of Pakistan, representing over 70% of national wheat production. Timely estimation of cultivated wheat area can serve to inform decision-making in managing harvests with regard to markets and food security. The current wheat area and yield reporting system, operated by the Punjab Crop Reporting Service (CRS) delivers crop forecasts several months after harvest. The delayed production data cannot contribute to in-season decision support systems. There is a need for an alternative cost-effective, efficient and timely approach on producing wheat area estimates, in ensuring food security for the millions of people in Pakistan. Landsat data, medium spatial and temporal resolutions, offer a data source for characterizing wheat in smallholder agriculture landscapes. This dissertation presents methods for operational mapping of wheat cultivate area using within growing season Landsat time-series data. In addition to maps of wheat cover in Punjab, probability-based samples of in-situ reference data were allocated using the map as a stratifier. A two-stage probability based cluster field sample was used to estimate area and assess map accuracies. The before-harvest wheat area estimates from field-based sampling and Landsat map were found to be comparable to official post-harvest data produced by the CRS Punjab. This research concluded that Landsat medium resolution data has sufficient spatial and temporal coverage for successful wall-to-wall mapping of wheat in Punjab’s smallholder agricultural system. Freely available coarse and medium spatial resolution satellite data such as MODIS and Landsat perform well in characterizing industrial cropping systems; commercial high spatial resolution satellite data are often advocated as an alternative for characterizing fine-scale land tenure agricultural systems such as that found in Punjab. Commercial 5 m spatial resolution RapidEye data from the peak of the winter wheat growing season were used as sub-pixel training data in mapping wheat with the growing season free 30 m Landsat time series data from the 2014-15 growing season. The use of RapidEye to calibrate mapping algorithms did not produce significantly higher overall accuracies ( ± standard error) compared to traditional whole pixel training of Landsat-based 30 m data. Continuous wheat mapping yielded an overall accuracy of 88% (SE = ±4%) in comparison to 87% (SE = ±4%) for categorical wheat mapping, leading to the finding that sub-pixel training data are not required for winter wheat mapping in Punjab. Given sufficient expertise in supervised classification model calibration, freely available Landsat data are sufficient for crop mapping in the fine-scale land tenure system of Punjab. For winter wheat mapping in Punjab and other similar landscapes, training data for supervised classification may be collected directly from Landsat images with probability based stratified random sampling as reference data without the need for high-resolution reference imagery. The research concluded by exploring the use of automated models in wheat area mapping and area estimation using growing season Landsat time-series data. The automated classification tree model resulted in wheat / not wheat maps with comparable accuracies compared to results achieved with traditional manual training. In estimating area, automated wheat maps from previous growing seasons can serve as a stratifier in the allocation of current season in-situ reference data, and current growing season maps can serve as an auxiliary variable in model-assisted area estimation procedures. The research demonstrated operational implementation of robust automated mapping in generating timely, accurate, and precise wheat area estimates. Such information is a critical input to policy decisions, and can help to ensure appropriate post-harvest grain management to address situations arising from surpluses or shortages in crop production.Item Investigating regional food hubs as tools for development and change: A multi-scale and mixed methods approach(2017) Motzer, Nicole; Silva, Julie A; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The revitalization of rural, agricultural communities in the United States represents a constant challenge. Persistently high levels of rural poverty stem in part from agricultural industrialization, the subsequent loss of family farms, and dwindling rural economies. Theoretically integrating economic viability, social justice, and environmental sustainability back into agriculture and food, alternative food networks (AFNs) represent opportunities for rural communities to redress social, economic, and environmental declines accompanying agricultural industrialization in the twentieth and twenty–first centuries. As organizations that aggregate, market, and distribute locally and regionally sourced food within wholesale, retail, and institutional markets, regional food hubs (RFHs) represent the most recent AFN type, but also the one most associated with advancing rural revitalization and agricultural change. An overall lack of empirical investigation, however, along with limited conceptualizations of development constrains current understandings as to how – or even if – RFHs contribute to rural development in the ways that are increasingly espoused in the literature and policy. With a focus on RFHs as rapidly expanding yet largely untested AFNs, this dissertation follows a mixed methods and multi–scale approach. Blending quantitative analyses at national and regional scales with qualitative case study data, this dissertation explores development–related potential and processes for RFHs in a variety of places and then empirically evaluates rural development outcomes in a theoretically ideal setting. Findings indicate that RFHs generally do not locate where outcomes are most likely to reflect rural development expectations, though to spatially varying degrees. When a RFH does locate in such a place, outcomes are primarily though not always positive, and overall suggest that RFHs can help to fill social, economic, and ecological gaps and needs. Results reveal that women farmers play integral roles in shaping and extending RFHs’ development impacts. Yet, persistent poverty and geographically concentrated disadvantages limit transformative capacities. Reigning in rural development claims, this dissertation concludes that although RFHs are unlikely to redress broad conditions of rural decline, they may prime rural, agricultural communities in ways that extend both the efficacy and reach of policies and interventions to follow.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 Prolonged Illness Among Subsistence Agricultural Households in Rural Mozambique: Coping Strategies and Policy Levers(2015) Dodson, Zan Michael; Silva, Julie A.; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Subsistence agriculturalists are highly economically vulnerable, and generally lack access to resources that may help strengthen their livelihoods. Health is a well-established form of human capital that is also one of the biggest assets for subsistence agricultural households. Therefore, any instance in which this form of capital is threatened has potential consequences for livelihood sustainability. This dissertation examined prolonged illness among subsistence agricultural households in rural Mozambique. Prolonged illness can diminish household labor supply, a vital input for subsistence agriculture. My research sought to: 1) identify potential agricultural and land use coping strategies used by unhealthy subsistence agricultural households, and examine whether or not changes in health status induce land cover change; 2) isolate health's effect on a known agricultural land use decision--fallowing--to more rigorously examine the negative health-land relationship; and 3) examine how a policy lever such as access to health services could be more equitably distributed to subsistence agricultural households. I found that unhealthy households were more likely to alter household agricultural land use decisions to cope with prolonged illness, and that they were different than their healthier counterparts in key agricultural ways that may threaten their livelihoods and contribute to food insecurity. While changes in health status do spur land use and land cover change, the relationship is challenging to detect with the current offering of satellite imagery. Additionally, access to health clinics represents a policy lever aimed at supporting unhealthy citizens to maintain their livelihoods. I found that the way "need" is defined in terms of access matters and that access to a high-quality service such as antiretroviral therapy could be more equitably distributed to vulnerable segments of society. This research demonstrates the value of using mixed methods, as the combination of qualitative, econometric, and geospatial methods, to provide a more holistic understanding of the micro-level effects of prolonged illness among subsistence agricultural households in rural Mozambique.Item Developing Earth Observations Requirements for Global Agricultural Monitoring: Toward a Multi-Mission Data Acquisition Strategy(2014) Whitcraft, Alyssa Kathleen; Justice, Christopher O; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Global food supply and our understanding of it have never been more important than in today's changing world. For several decades, Earth observations (EO) have been employed to monitor agriculture, including crop area, type, condition, and yield forecasting processes, at multiple scales. However, the EO data requirements to consistently derive these informational products had not been well defined. Responding to this dearth, I have articulated spatially explicit EO requirements with a focus on moderate resolution (10-70m) active and passive remote sensors, and evaluate current and near-term missions' capabilities to meet these EO requirements. To accomplish this, periods requiring monitoring have been identified through the development of agricultural growing season calendars (GSCs) at 0.5 degrees from MODIS surface reflectance. Second, a global analysis of cloud presence probability and extent using MOD09 daily cloud flags over 2000-2012 has shown that the early-to-mid agricultural growing season (AGS) - an important period for monitoring - is more persistently and pervasively occluded by clouds than is the late and non-AGS. Third, spectral, spatial, and temporal resolution data requirements have been developed through collaboration with international agricultural monitoring experts. These requirements have been spatialized through the incorporation of the GSCs and cloud cover information, establishing the revisit frequency required to yield reasonably clear views within 8 or 16 days. A comparison of these requirements with hypothetical constellations formed from current/planned moderate resolution optical EO missions shows that to yield a scene at least 70% clear within 8 or 16 days, 46-55% or 10-32% of areas, respectively, need a revisit more frequent than Landsat 7 & 8 combined can deliver. Supplementing Landsat 7 & 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. No single mission/observatory can consistently meet requirements throughout the year, and the only way to meet a majority (77-94% for ≥70% clear; 47-73% for 100% clear) of 8 day requirements is through coordination of multiple missions. Still, gaps exist in persistently cloudy regions and periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring.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.Item The impact of agricultural irrigation on land surface characteristics and near surface climate in China(2012) Zhu, Xiufang; Liang, Shunlin; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)It is well known that land cover and land use change can significantly influence the climate system by modulating surface-atmosphere exchanges. Land management, such as irrigation, also has a profound influence on the climate system. Irrigation can alter the water and energy flux from ground surface to the atmosphere and further influence near surface climate. Considering its dramatic expansion during the last century, the widespread use of irrigation has had an ongoing impact on our climate system. However, until now, this relationship between increased irrigation and its effect on climate system has not been well examined. The main objective of this dissertation is to quantify the irrigation impacts on land surface characteristics and near surface climate over China by using both observational (remote sensing and meteorological observation) and modeling studies with four specific questions: Where are the irrigated areas in China? What might have happened in the past? What will happen as a result of irrigation expansion in the future? And what is the relationship between the land cover land use change (LCLUC) impact and the irrigation impact on near surface climate in China? To answer these questions, I 1) developed three irrigation potential indices and produced a high resolution irrigation map of China; 2)analyzed and compared meteorological and remote sensing observations in irrigated and non-irrigated agriculture areas of China; 3) simulated both irrigation and LCLUC impact on land surface energy balance components (i.e., land surface temperature, latent flux, and sensible flux) and near surface climate (i.e., air temperature, water vapor, relative humidity) of China in the past (1978-2004) and also in two future time periods (2050 and 2100) by using the Community Land Model and compared the impact of irrigation with that of LUCC. Meteorological observations in Jilin Province show that the temperature differences between highly and lightly irrigated areas are statistically significant. The differences are highly correlated with the effective irrigation area (EIA) and sown area of crop (CSA). Results from satellite observations show that highly irrigated areas corresponded to lower albedo and daytime land surface temperature (LST), and higher normalized difference vegetation index (NDVI) and evapotranspiration (ET). The difference between highly and lightly irrigated areas is bigger in drier areas and in drier years. The modeling studies show that the irrigation impact on temperature is much less in the future than in the 20th century and that irrigation impacts more on the maximum air temperature than on the minimum air temperature. Both contemporary and future irrigation simulations show, nationally, irrigation decreases daily maximum temperature (Tmax) but increase daily minimum temperature (Tmin). Daily mean temperature (Tmean) decreases in contemporary irrigation simulations but increases in most of the cases in future irrigation simulations. In the 20th century, nationally, the spray irrigation leads to a decrease in Tmax of 0.079K and an increase in Tmin of 0.022K. Nationally, the spray irrigation leads to a decrease in Tmax between 0.022K and 0.045K and an increase in Tmin between 0.019K and 0.057K under future scenarios. This study demonstrates that the irrigation patterns (flood irrigation and spray irrigation) have statistically significant impacts on local climate. Moreover, this study suggests that, in the national respective, the impacts of changes in land management on climate are not comparable to the impacts of changes in land cover land use. This dissertation on irrigation and its impact is the first study which focuses solely on China using observational and modeling methods. The results from this dissertation contribute to a better understanding of the irrigation impact on near-surface climate which can improve our knowledge of how human activities influence climate, guide future policies aimed at mitigating or adapting to climate change, and help design a precise model to project the impact of irrigation on the climate system and irrigation requirements in the future. It can also be useful in assessing future food and water security issues.Item Health, Agriculture and Labor Markets in Developing Countries(2010) Kim, Yeon Soo; Cropper, Maureen; Lafortune, Jeanne; Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rural households comprise a large share of the population in developing countries. This dissertation examines how the welfare of these households, whose economic activity mainly relies on agriculture, is affected by weather shocks and health shocks in the context of West Africa and Vietnam. In the second chapter of the dissertation, I use the variation in rainfall within and across years at a detailed geographic level in West Africa to examine how rainfall shocks might affect the well-being of very young children. Variations in rainfall may affect not only income, but also the opportunity cost of time of parents, which may negatively impact child welfare. I find that high long-term rainfall averages for a particular location and month increase the probability of giving birth in the dry season, whereas positive deviations from this long-term mean ("rainfall shocks") have a small but statistically significant negative effect on the probability of giving birth in the rainy season. Further, contrary to what one might expect, rainfall shocks do not appear to improve the survival chances of young children and shocks in the first year of life have an adverse effect on the survival of children that are born in the rainy season. This result may be partly attributable to the finding that rainfall shocks significantly reduce the time mothers breastfeed their children, which could be due to a trade-off with work. Breastfeeding is important for the health of young children since it provides not only essential nutrients but also effective protection against various diseases. In the third chapter, I examine the effect of health shocks on the production decisions of agricultural households in Vietnam. I look at whether malaria illnesses experienced by the household have an effect on their agricultural production decisions. While I am not able to entirely overcome issues with endogeneity that are persistent in this literature, results show that profits are negatively associated with the share of household members experiencing malaria. This result is not explained by the decrease in the total number of labor days the household employed. Rather, households appear to change their crop choice to less labor-intensive, less profitable crops in anticipation of these seasonal health shocks.