A GENERALIZED APPROACH TO WHEAT YIELD FORECASTING USING EARTH OBSERVATIONS: DATA CONSIDERATIONS, APPLICATION, AND RELEVANCE.

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

Landsat-based operational wheat area estimation model for Punjab, Pakistan

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

Essays on the economics of salinity in irrigated agriculture

Doctor of PhilosophyDepartment of Agricultural EconomicsNathan P. HendricksSalinity has become a major concern in irrigated agriculture. Degraded water quality due to salinity threatens agricultural sustainability by limiting agricultural productivity and profitability. The natural intrusion of saltwater into aquifers is one reason for salinity, yet groundwater overpumping for irrigated agriculture, and regional climate and hydrology conditions have compounded such salinity challenges. The sustainability of irrigated agriculture in a saline environment depends on an accurate understanding of the effects of salinity and potential methods of adaptation. This dissertation contains three chapters providing insights into how salinity impacts agricultural decisions and land values. These insights are obtained by analyzing two regions, the High Plains Aquifer in central Kansas and the Central Valley Aquifer in California. The first chapter examines the impact of groundwater salinity on farmers’ main irrigation decisions by estimating the response along the extensive (i.e., irrigated acres), direct intensive (i.e., water application depth), and indirect intensive (i.e., crop choice) margins. Econometrics models are estimated using observed farmer behavior in response to exposure to different groundwater salinity levels using field-level panel data in a region of Kansas during 1991–2014. Results demonstrate that farmers facing salinity adjust their water use through all three margins, but most of the decrease in water use due to higher salinity is through the extensive margin. The second chapter evaluates the impact of groundwater salinity on agricultural land values with a unique dataset of parcel sale prices during 1988–2015 in a region of Kansas. I estimate hedonic regression models that control for spatial heterogeneity using either county fixed-effects or a nonlinear function of the geographic coordinates. The results demonstrate that groundwater salinity negatively impacts land values. These estimates can be interpreted as the economic damages from salinity, or equivalently, farmers’ willingness-to-pay to offset salinity. The third chapter quantifies the adaption to soil salinity by farmers in California’s Western San Joaquin Valley by econometrically estimating how farmers change crop choices in response to different soil salinity levels. I use high-resolution remotely-sensed soil salinity and crop data during 2007–2016. My estimates show that as the level of salinity increases, the probability of growing salt-tolerant crops increases. This suggests that farmer’ adapt to salinity according to the degree of salinity. However, my estimates may have some endogeneity bias since crop choice affects the amount of water applied, which could affect the amount of soil salinity

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio

Review of existing information on the interrelations between soil and climate change. (ClimSoil). Final report

Carbon stock in EU soils – The soil carbon stocks in the EU27 are around 75 billion tonnes of carbon (C); of this stock around 50% is located in Sweden, Finland and the United Kingdom (because of the vast area of peatlands in these countries) and approximately 20% is in peatlands, mainly in countries in the northern part of Europe. The rest is in mineral soils, again the higher amount being in northern Europe. 2. Soils sink or source for CO2 in the EU – Both uptake of carbon dioxide (CO2) through photosynthesis and plant growth and loss of CO2 through decomposition of organic matter from terrestrial ecosystems are significant fluxes in Europe. Yet, the net terrestrial carbon fluxes are typically 5-10 times smaller relative to the emissions from use of fossil fuel of 4000 Mt CO2 per year. 3. Peat and organic soils - The largest emissions of CO2 from soils are resulting from land use change and especially drainage of organic soils and amount to 20-40 tonnes of CO2 per hectare per year. The most effective option to manage soil carbon in order to mitigate climate change is to preserve existing stocks in soils, and especially the large stocks in peat and other soils with a high content of organic matter. 4. Land use and soil carbon – Land use and land use change significantly affects soil carbon stocks. On average, soils in Europe are most likely to be accumulating carbon on a net basis with a sink for carbon in soils under grassland and forest (from 0 - 100 billion tonnes of carbon per year) and a smaller source for carbon from soils under arable land (from 10 - 40 billion tonnes of carbon per year). Soil carbon losses occur when grasslands, managed forest lands or native ecosystems are converted to croplands and vice versa carbon stocks increase, albeit it slower, following conversion of cropland. 5. Soil management and soil carbon – Soil management has a large impact on soil carbon. Measures directed towards effective management of soil carbon are available and identified, and many of these are feasible and relatively inexpensive to implement. Management for lower nitrogen (N) emissions and lower C emissions is a useful approach to prevent trade off and swapping of emissions between the greenhouse gases CO2, methane (CH4) and nitrous oxide (N2O). 6. Carbon sequestration – Even though effective in reducing or slowing the build up of CO2 in the atmosphere, soil carbon sequestration is surely no ‘golden bullet’ alone to fight climate change due to the limited magnitude of its effect and its potential reversibility; it could, nevertheless, play an important role in climate mitigation alongside other measures, especially because of its immediate availability and relative low cost for 'buying' us time. 7. Effects of climate change on soil carbon pools – Climate change is expected to have an impact on soil carbon in the longer term, but far less an impact than does land use change, land use and land management. We have not found strong and clear evidence for either overall and combined positive of negative impact of climate change (atmospheric CO2, temperature, precipitation) on soil carbon stocks. Due to the relatively large gross exchange of CO2 between atmosphere and soils and the significant stocks of carbon in soils, relatively small changes in these large and opposing fluxes of CO2, i.e. as result of land use (change), land management and climate change, may have significant impact on our climate and on soil quality. 8. Monitoring systems for changes in soil carbon – Currently, monitoring and knowledge on land use and land use change in EU27 is inadequate for accurate calculation of changes in soil carbon contents. Systematic and harmonized monitoring across EU27 and across relevant land uses would allow for adequate representation of changes in soil carbon in reporting emissions from soils and sequestration in soils to the UNFCCC. 9. EU policies and soil carbon – Environmental requirements under the Cross Compliance requirement of CAP is an instrument that may be used to maintain SOC. Neither measures under UNFCCC nor those mentioned in the proposed Soil Framework Directive are expected to adversely impact soil C. EU policy on renewable energy is not necessarily a guarantee for appropriate (soil) carbon management

Impervious Surfaces Mapping Using High Resolution Satellite Imagery

In recent years, impervious surfaces have emerged not only as an indicator of the degree of urbanization, but also as an indicator of environmental quality. As impervious surface area increases, storm water runoff increases in velocity, quantity, temperature and pollution load. Any of these attributes can contribute to the degradation of natural hydrology and water quality. Various image processing techniques have been used to identify the impervious surfaces, however, most of the existing impervious surface mapping tools used moderate resolution imagery. In this project, the potential of standard image processing techniques to generate impervious surface data for change detection analysis using high-resolution satellite imagery was evaluated. The city of Oxford, MS was selected as the study site for this project. Standard image processing techniques, including Normalized Difference Vegetation Index (NDVI), Principal Component Analysis (PCA), a combination of NDVI and PCA, and image classification algorithms, were used to generate impervious surfaces from multispectral IKONOS and QuickBird imagery acquired in both leaf-on and leaf-off conditions. Accuracy assessments were performed, using truth data generated by manual classification, with Kappa statistics and Zonal statistics to select the most appropriate image processing techniques for impervious surface mapping. The performance of selected image processing techniques was enhanced by incorporating Soil Brightness Index (SBI) and Greenness Index (GI) derived from Tasseled Cap Transformed (TCT) IKONOS and QuickBird imagery. A time series of impervious surfaces for the time frame between 2001 and 2007 was made using the refined image processing techniques to analyze the changes in IS in Oxford. It was found that NDVI and the combined NDVI–PCA methods are the most suitable image processing techniques for mapping impervious surfaces in leaf-off and leaf-on conditions respectively, using high resolution multispectral imagery. It was also found that IS data generated by these techniques can be refined by removing the conflicting dry soil patches using SBI and GI obtained from TCT of the same imagery used for IS data generation. The change detection analysis of the IS time series shows that Oxford experienced the major changes in IS from the year 2001 to 2004 and 2006 to 2007

Rain Induced Runoff Simulation Using A 2D Numerical Model

Runoff plays an important role in the agricultural and urbanized environments. Surface runoff is determined primarily by the amount of precipitation and by infiltration characteristics related to soil type, soil moisture, antecedent rainfall, land cover type, impervious surfaces, and surface retention. In this study, CCHE2D, a numerical model for free surface flow hydrodynamics, is applied to study the rain induced runoff and channel flow mixed problems. The main goal of this study is to incorporate rainfall as an input into the existing free surface flow model, CCHE2D; to verify and validate the CCHE2D model’s runoff simulation capability using analytical solutions and experimental data so that the model is proven to be accurate and capable of simulating rainfall induced flow and runoff; and to simulate runoff process in complex watershed using high resolution data such as LiDAR topography to validate that the model can be applicable to problems with a variety of spatial scales and complexity. Infiltration and subsurface flow is not considered throughout the study. The model’s capability of simulating the rainfall generated runoff processes is tested using analytical solutions, experimental data and field data. Comparison of numerical solutions with both analytic solutions and observed overland flows resulting from unsteady rainfalls is satisfactory. To validate the applicability of the shallow water model CCHE2D, research has been conducted using very high resolution LiDAR data in a small real world agricultural watershed in northwestern Mississippi, USA, in the Mississippi River Alluvial Plain known as the Mississippi Delta. The fine resolution of the numerical simulations resolved detailed runoff patterns in watersheds. This capability can be used for soil erosion and agro-pollutant transport and flood impact studies