Climate change presents a clear threat to the future of global food security. Changes in the patterns of temperature and precipitation have the potential to greatly decrease agri- cultural production. Developing successful adaptation strategies is dependent on under- standing both the potential changes in yield of a given crop, as well as the likelihood those changes occurring. This requires an understanding of the uncertainty in the geographic patterns of future climate change, as well as the response of a crop to those changes. In this dissertation I explore a framework for generating rapid estimates of the risk of climate change to agricultural yields. Using data from multiple climate models I use a regression based pattern scaling ap- proach in conjunction with a multi-resolution Gaussian spatial process model to emulate the output of a multi-model ensemble of global climate models. The approach is flexible across climate scenarios, allowing it to be easily used in conjunction with other impact models. Using this model I am able to rapidly emulate thousands of runs of a climate model on a laptop computer. The resulting synthetic distributions retain the spatial variability of the initial emulated models and provide a tool for generating probabilistic forecasts of regional climate change. Next I use a generalized additive model approach to estimate the stable manifold yield response surface of a set of irrigated and rained crops in China. This approach highlights the nonlinear relationship between changes in temperature and precipitation and yield. Results suggest that irrigation alone cannot prevent losses from climate change. Predictions of future temperature and precipitation show a trend towards temperatures above the critical threshold for many crops, indicating the potential for large losses. In the final chapter I combine the previously described methods to assess the impact of climate change on the spatial patterns of crop yield change in China. Result indicate overall losses to crop yield in the majority of cropped regions for both irrigated and non irrigated crops. These results represent a new methodology for rapidly assessing the risk of climate change to crop yield, and provide a new tool for prioritizing adaptation measures.

Multispectral aerial and satellite remote sensing plays a major role in crop yield prediction due to its ability to detect crop growth conditions on spatial and temporal scales in a cost effective manner. Many empirical relationships have been established in the past between spectral vegetation indices and leaf area index, fractional ground cover, and crop growth rates for different crops through ground sampling. Remote sensing-based vegetation index (VI) yield models using airborne and satellite data have been developed only for grain crops like barley, corn, wheat, and sorghum. So it becomes important to validate and extend the VI-based model for tuber crops like potato, taking into account the most significant parameters that affect the final crop yield of these crops.

Biochars are the product of high temperature treatment of carbonaceous materials with little or no oxygen present, termed “pyrolysis”. Biochars derived from the pyrolysis of biomass feedstocks have proven effective amendments on highly weathered tropical soils. However less is known about their impact on temperate soils and associated crop growth. Moreover, there is inadequate knowledge of the impacts of different biochars produced from different feedstocks under differing pyrolysis conditions. Therefore, a study was conducted to evaluate the effectiveness of different biochars as amendments to improve soil conditions for crop growth, with emphasis on soil fertility and crop nutrition impacts. The response of canola-wheat in rotation to five biochars was evaluated in controlled environment and field experiments conducted on Brown and Black Chernozem soils over a two-year period. Treatments were biochar added at 1 and 2 t ha 1 without and with nitrogen (N) and phosphorus (P) fertilizers at 50 or 100 kg N ha 1 and 25 kg P2O5 ha 1. Parameters evaluated were crop biomass and grain yield, N and P uptake, % recovery of applied N and P, residual soil nutrients (NO3 N, and PO4+ P), pH, electrical conductivity (EC), % organic carbon (% OC) and gravimetric soil moisture. Biochar application resulted in significant increases (p<0.05) in canola yield compared to the control for two fast pyrolysis biochars originating from wheat and flax straw added to the Black Chernozem soil in both studies. No significant response was observed for any of the biochars on the Brown Chernozem. Slow pyrolysis biochar derived from willow feedstock appeared less effective did not show any significant response. Occasional depressions in crop yield were observed in both crops with both soils. In these calcareous Chernozems, biochar did not greatly alter the N and P availability, and its effects on soil pH, % OC, EC and moisture content were small and often non-significant. These results suggest that biochar applications at 1 2 t ha 1 to prairie Chernozemic soils will not have large effects on soil properties or plant growth. Higher rates of application will require development of application technology due to the dusty, powdery nature of the biochar material.

This thesis examines a newly proposed density estimator in order to evaluate its usefulness for government crop insurance programs confronted by the problem of adverse selection. While the Federal Crop Insurance Corporation (FCIC) offers multiple insurance programs including Group Risk Plan (GRP), what is needed is a more accurate method of estimating actuarially fair premium rates in order to eliminate adverse selection. The Empirical Bayes Nonparametric Kernel Density Estimator (EBNKDE) showed a substantial efficiency gain in estimating crop yield densities. The objective of this research was to apply EBNKDE empirically by means of a simulated game wherein I assumed the role of a private insurance company in order to test for profit gains from the greater efficiency and accuracy promised by using EBNKDE. Employing EBNKDE as well as parametric and nonparametric methods, premium insurance rates for 97 Illinois counties for the years 1991 to 2010 were estimated using corn yield data from 1955 to 2010 taken from the National Agricultural Statistics Service (NASS). The results of this research revealed substantial efficiency gain from using EBNKDE as opposed to other estimators such as Normal, Weibull, and Kernel Density Estimator (KDE). Still, further research using other crops yield data from other states will provide greater insight into EBNKDE and its performance in other situations.

Sediment and nutrient loss from agricultural landscapes contributes to water quality impairment and has the potential to impact crop yield. Best management practices (BMPs) such as riparian buffers have been designed to combat these issues; however, concentrated flow paths (CFPs) reduce their effectiveness and are often overlooked in agricultural fields. Conventional management of CFPs is to fill and grade them, however this provides only a short term solution leading to their reformation and increased sediment loss. The objectives of this project were: i) to determine if the filling of CFPs influence crop growth (yield and biomass), ii) determine a distance at which crop growth is no longer influenced by CFPs, iii) assess the impact that topography and CFPs have on crop growth, iv) analyze water quality in surface runoff leaving crop fields via CFPs, and v) develop an economic analysis for CFP’s influence on crop returns. Six small agricultural catchments, CFPs, and topographic positions (i.e., depositional, backslope, and shoulder) were delineated using ArcGIS and LiDAR data. In each catchment, six 4 m2 plots were established along CFPs where crop biomass and crop yield were measured. Additionally, six plots with no influence from CFPs were established as reference plots. Surface water quality was assessed by taking edge-of-field grab samples at the CFP outlet during significant rain events (i.e., precipitation exceeding 2.5 cm). Water samples were analyzed for total suspended solids (TSS), total phosphorus (TP), dissolved reactive phosphorus (DRP), ammonium-nitrogen (NH4+-N), and nitrate-N (NO3- -N). Through this study it was shown that CFPs served as a conduit for transporting nutrient and sediment laden runoff to receiving waters, that increasing/decreasing distance from CFPs had an impact on crop yields, and that there was no crop yield advantage from the filling of CFPs vs. leaving them unfilled. Median values for NO3-N (1.85 mg L-1) and TSS (140 mg L-1) in the Fill catchments were higher than the No-Fill catchments (0.77 mg L-1 and 35.5 mg L-1, respectively), while DRP and TP concentrations were higher in the No-Fill catchments (1.31 mg L-1 and 2.37 mg L-1, respectively) compared to the Fill catchments (0.91 mg L-1 and 1.83 mg L-1, respectively) over the growing season. Crop biomass and yield results between the depositional and backslope positions were similar regardless of treatment, but were lower than the reference plots and shoulder position. Results from the economic analysis on the cost of farming in/near CFPs indicated that the economic return was greatly dependent on precipitation. CFPs are generally concave positions on the landscapes and have been eroded to a clayey subsoil, both resulting in greater water accumulation and retention than elsewhere in the field. During wetter years, an economic loss was incurred nearest to the CFP and during drier years, sites nearest to CFPs saw positive returns. Farmers and land managers may consider implementing stabilization measures, such as grassed waterways, in CFPs since crop yields are typically lower in wetter years, there’s increased cost to maintain these areas, and accelerated sediment loss can exacerbate the crop yield losses and impact on water quality.

Water shortage is the most important factor constraining agricultural production all over the world. New irrigation strategies must be established to use the limited water resources more efficiently. This study was carried out in a completely randomized design with three replications under the greenhouse condition at Shahrekord University, Shahrekord, Iran. In this study, the physiological responses of pepper plant affected by irrigation water were investigated. Irrigation treatments included control (full irrigation level, FI) and three deficit irrigation levels, 80, 60 and 40% of the plant’s water requirement called DI80, DI60, and DI40, respectively. A no plant cover treatment with three replications was also used to measure evaporation from the soil surface. Daily measurements of volumetric soil moisture (VSM) were made at each 10 cm intervals of the soil column, considered as a layer. The differences between the measured VSM and the VSM in the next day and evaporation rate at the soil surface at the same layer of the no plant cover treatment were calculated. Eventually, by considering the applied and collected water in each treatment, evapotranspiration (ETC) and root water uptake (RWU) in each layer per day were estimated. Furthermore, fruit number per plant, fresh fruit weight/day, root fresh/dry weight, shoot fresh/dry weight, root zone volume, root length and density, crop yield, and water use efficiency (WUE) were measured under different water treatments. The results showed that the maximum and minimum of all the studied parameters were found in the FI and DI40 treatments, respectively. ETC in the DI80, DI60, and DI40 treatments were reduced by 14.2, 37.4, and 52.2%, respectively. Furthermore, applying 80, 60, and 40% of the plant’s water requirement led to crop yield reduction by 29.4, 52.7, and 69.5%, respectively. The averages of root water uptakes (ARWUs) in the DI80, DI60, and DI40 treatments reduced by 17.08, 48.72, and 68.25%, respectively. WUE and crop yield also showed no significant difference in the FI and DI80 treatments. Moreover, in the DI80 treatment the reduced rate of water uptake was less than the reduced rate of plant's applied water. According to these results, it can be concluded that 20% deficit irrigation had no significant reduction on the yield of pepper, but above this threshold, there was an adverse effect on the growth and yield. Therefore, for water management in the regions with limited water resources, plant's applied water can be decreased around 20%.

By 2050, food consumption and agricultural water use will increase as a result of a global population that is projected to reach 9 billion people. To address this food and water security challenge, there has been increased attention towards the concept of sustainable agriculture, which has a broad aim of securing food and water resources while preserving the environment for future generations. An element of this is the use of precision agriculture, which is designed to provide the right inputs, at the right time and in the right place. In order to optimize nutrient application, water intakes, and the profitability of agricultural areas, it is necessary to improve our understating and predictability of agricultural systems at high spatio-temporal scales. The underlying goal of the research presented herein is to advance the monitoring of croplands and crop yield through high-resolution satellite data. In addressing this, we explore the utility of daily CubeSat imagery to produce the highest spatial resolution (3 m) estimates of leaf area index and crop water use ever retrieved from space, providing an enhanced capacity to provide new insights into precision agriculture. The novel insights on crop health and conditions derived from CubeSat data are combined with the predictive ability of crop models, with the aim of improving crop yield predictions. To explore the latter, a sensitivity analysis-linked Bayesian inference framework was developed, offering a tool for calibrating crop models while simultaneously quantifying the uncertainty in input parameters. The effect of integrating higher spatio-temporal resolution data in crop models was tested by developing an approach that assimilates CubeSat imagery into a crop model for early season yield prediction at the within-field scale. In addition to satellite data, the utility of even higher spatial resolution products from unmanned aerial vehicles was also examined in the last section of the thesis, where future research avenues are outlined. Here, an assessment of crop height is presented, which is linked to field biomass through the use of structure from motion techniques. These results offer further insights into small-scale field variabilities from an on-demand basis, and represent the cutting-edge of precision agricultural advances.

<p>The study concerns the development of a methodology which will allow the rating of soil or land units in view of their sustained economic capacity. Some literature is surveyed to show that no known scheme justifies statistically the number of classes used;many schemes avoid the use of empirical crop yield data altogether. The factor analysis of farm activities and crop yield data will provide a set of scores, incorporating the important variables only, which may be grouped by the statistical method ·which is the core of the work. The Andean portion of the Province of Pichihcha, Ecuador, was the area studied. A number of farm types were discovered, differentiated by levels of investment and subsequently by type of activity. The strong crop yield/environmental correlations needed to create an improved land use capability scheme were found not to exist, but some important observations were made upon the profitability of the crop holdings and the fertility status of the soils. A number of land types with regional tendencies were found.</p> / Thesis / Master of Arts (MA)

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.

Farming communities in the United States and around the world lose billions of dollars every year due to drought. Drought Indices such as the Palmer Drought Severity Index (PDSI) and Standardized Precipitation Index (SPI) are widely used by the government agencies to assess and respond to drought. These drought indices are currently monitored at a large spatial resolution (several thousand km2). Further, these drought indices are primarily based on precipitation deficits and are thus good indicators for monitoring large scale meteorological drought. However, agricultural drought depends on soil moisture and evapotranspiration deficits. Hence, two drought indices, the Evapotranspiration Deficit Index (ETDI) and Soil Moisture Deficit Index (SMDI), were developed in this study based on evapotranspiration and soil moisture deficits, respectively. A Geographical Information System (GIS) based approach was used to simulate the hydrology using soil and land use properties at a much finer spatial resolution (16km2) than the existing drought indices. The Soil and Water Assessment Tool (SWAT) was used to simulate the long-term hydrology of six watersheds located in various climatic zones of Texas. The simulated soil water was well-correlated with the Normalized Difference Vegetation Index NDVI (r ~ 0.6) for agriculture and pasture land use types, indicating that the model performed well in simulating the soil water. Using historical weather data from 1901-2002, long-term weekly normal soil moisture and evapotranspiration were estimated. This long-term weekly normal soil moisture and evapotranspiration data was used to calculate ETDI and SMDI at a spatial resolution of 4km ?? 4km. Analysis of the data showed that ETDI and SMDI compared well with wheat and sorghum yields (r > 0.75) suggesting that they are good indicators of agricultural drought. Rainfall is a highly variable input both spatially and temporally. Hence, the use of NEXRAD rainfall data was studied for simulating soil moisture and drought. Analysis of the data showed that raingages often miss small rainfall events that introduce considerable spatial variability among soil moisture simulated using raingage and NEXRAD rainfall data, especially during drought conditions. The study showed that the use of NEXRAD data could improve drought monitoring at a much better spatial resolution.