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Browsing by Author "Khosla, Rajiv, advisor"

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    Active sensing: an innovative tool for evaluating grain yield and nitrogen use efficiency of multiple wheat genotypes
    (Colorado State University. Libraries, 2012) Naser, Mohammed Abdulridha, author; Khosla, Rajiv, advisor; Haley, Scott, committee member; Reich, Robin, committee member
    Precision agricultural practices have significantly contributed to the improvement of crop productivity and profitability. Remote sensing based indices, such as Normalized Difference Vegetative Index (NDVI) have been used to obtain crop information. It is used to monitor crop development and to provide rapid and nondestructive estimates of plant biomass, nitrogen (N) content and grain yield. Remote sensing tools are helping improve nitrogen use efficiency (NUE) through nitrogen management and could also be useful for high NUE genotype selection. The objectives of this study were: (i) to determine if active sensor based NDVI readings can differentiate wheat genotypes, (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes, (iii) to identify and quantify the main sources of variation in NUE across wheat genotypes, and (iv) to determine if normalized difference vegetation index (NDVI) could characterize variability in NUE across wheat genotypes. This study was conducted in north eastern Colorado for two years, 2010 and 2011. The NDVI readings were taken weekly during the winter wheat growing season from March to late June, in 2010 and 2011 and NUE were calculated as partial factor productivity and as partial nitrogen balance at the end of the season. For objectives i and ii, the correlation between NDVI and grain yield was determined using Pearson's product-moment correlation coefficient (r) and linear regression analysis was used to explain the relationship between NDVI and grain yield. The K-means clustering algorithm was used to classify mean NDVI and mean grain yield into three classes. For objectives iii and iv, the parameters related to NUE were also calculated to measure their relative importance in genotypic variation of NUE and power regression analysis between NDVI and NUE was used to characterize the relationship between NDVI and NUE. The results indicate more consistent association between grain yield and NDVI and between NDVI and NUE later in the season, after anthesis and during mid-grain filling stage under dryland and a poor association in wheat grown in irrigated conditions. The results suggest that below saturation of NDVI values (about 0.9), (i.e. prior to full canopy closure and after the beginning of senescence or most of the season under dryland conditions) NDVI could assess grain yield and NUE. The results also indicate that nitrogen uptake efficiency was the main source of variation of NUE among genotypes grown in site-years with lower yield. Overall, results from this study demonstrate that NDVI readings successfully classified wheat genotypes into grain yield classes across dryland and irrigated conditions and characterized variability in NUE across wheat genotypes.
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    Ground based active remote sensors for precision nitrogen management in irrigated maize production
    (Colorado State University. Libraries, 2009) Shaver, Timothy Michael, author; Westfall, Dwayne G., advisor; Khosla, Rajiv, advisor
    Precision agriculture can increase farm input efficiency by accurately quantifying variability within a field. Remotely sensed normalized difference vegetation index (NDVI) has been shown to quantify maize (Zea mays) N variability. Ground-based active remote sensors that can determine NDVI are commercially available and have been shown to accurately distinguish N variability in maize. There are several active sensors available but no studies directly comparing active sensors have been reported. Therefore, a study was conducted to evaluate active sensor performance and develop an in-season maize N recommendation algorithm for use in Colorado using NDVI. Previous studies have demonstrated an association of active sensor NDVI with maize N content and height. However, the NDVI from a GreenSeekerâ„¢ green NDVI prototype active sensor had not yet been tested when our study began. Therefore, the green sensor was evaluated to determine if differences in plant growth across MZ could be determined by the active sensor. Results show that the prototype active sensor did not record NDVI values that were associated with MZ. The NDVI from two different sensors (Crop Circleâ„¢ amber NDVI and GreenSeekerâ„¢ red NDVI) were then examined under greenhouse and field conditions. Results show that NDVI from the amber and red sensors equally distinguished applied N differences in maize. Each active sensor's NDVI values had high R2 values with applied N rate and plant N concentration. Results also show that each sensor's NDVI readings had high R2 values with applied N rate and yield at the V12 and V14 maize growth stages. An N recommendation algorithm was then created for use at the V12 maize growth stage for both the amber and red sensors using NDVI. These algorithms yielded N recommendations that were not significantly different across sensor type suggesting that the amber and red NDVI sensors performed equally. Also, each N recommendation algorithm yielded unbiased N recommendations suggesting that each was a valid estimator of required N at maize growth stage V12. Overall results show that the amber and red sensors equally determine N variability in irrigated maize and could be very important tools for managing in-season application of N fertilizer.
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    Precision manure management across site-specific management zones
    (Colorado State University. Libraries, 2009) Moshia, Matshwene Edwin, author; Khosla, Rajiv, advisor
    In the western Great Plains of the USA, animal agriculture is an important contributor to the agricultural economy, and many livestock farms are close to water bodies where manure can potentially contaminate the environment. The objectives of the study were to (i) assess the influence of variable rate applications of animal manure on grain yield in continuous maize production fields across management zones (MZs) in dryland and limited irrigation cropping systems, (ii) to study the effects of variable rate application of animal manure on selected surface soil quality parameters across MZs, (iii) to evaluate the variable rate application of manure using environmental risk assessment tools of N leaching and P runoff indices and to understand its impact on environmental quality, and (iv) to evaluate and compare the nitrogen (N) mineralization of variable rates of dairy cattle manure applied on low, medium and high MZs in a controlled environment. To accomplish objectives (i) through (iii), the study was conducted under a continuous maize cropping system on dryland and limited furrow-irrigated fields in northeastern Colorado, USA. For objective (iv), a 120 day laboratory incubation study was conducted. The results of this project indicated that using animal manure alone for maize grain yield production was economically inefficient using enterprise budget analysis. The study suggests that manure can, therefore, be used in conjunction with synthetic N fertilizer to meet crop N requirements at early growth of maize, while animal manure improve soil quality of low productivity soils over time. This can potentially help to limit the amount of N and P lost into the environment. For N mineralization, the study showed a significant difference (P≤0.05) in mineralized N across zones when dairy animal manure treatments were compared. However, N from animal manure does not mineralize differently between low, medium and high management zones. The key in precision manure management was to find a balance between economically, agronomically and environmentally sound manure management strategies across spatially variable soils.
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    Spatial dynamics of weeds in irrigated corn
    (Colorado State University. Libraries, 2011) O'Meara, Scott, author; Westra, Phil, advisor; Khosla, Rajiv, advisor; Brown, Cynthia, committee member; Reich, Robin, committee member
    To view the abstract, please see the full text of the document.
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    Spatial modeling of site productivity and plant species diversity using remote sensing and geographical information system
    (Colorado State University. Libraries, 2011) Mohamed, Adel Ahmed Hassan, author; Reich, Robin M., advisor; Khosla, Rajiv, advisor; Andales, Allan, committee member; Wei, Yu, committee member
    The primary objective of this study was to describe the variability in site productivity of the diverse forests found in the state of Jalisco, Mexico. This information is fundamental for the management and sustainability of the species-rich forests in the state. The study also contributes to developing conservation-management program for the plant species diversity in Elba protected area in Egypt. The objective of chapter 1 was to develop site productivity index (SPI) curves for eight major forest types in the state of Jalisco, Mexico, using the height-diameter relationship of the dominant trees. Using permanent plot data, selected height-diameter functions were evaluated for their predictive performance within each of the major forest types. An important finding of this study was that a simple linear model could be used to describe the height-diameter relationship of the dominant trees in all of the major forest types considered in this study. SPI varied significantly among forest types, which are largely determined by the trends in temperature and precipitation. SPI decreased with increasing temperature and increased with increasing precipitation. The height-diameter relationship of the dominant trees was independent of stand density, and the more productive sites are able to sustain higher levels of basal area and volume, than the less productive sites. Trees on more productive sites had less taper than trees on less productive sites; and stand density did not influence the form or taper of the dominant trees. Chapter 2 evaluates methods to model the spatial distribution of site productivity in eight major forest types found in the state of Jalisco, Mexico. A site productivity index (SPI) based on the height-diameter relationship of dominant trees was used to estimate the site productivity of 818 forests plots located throughout the state. A combination of regression analysis and a tree-based stratified design was used to describe the relationship between SPI and environmental variables which included soil attributes (pH, sand, and silt), topography (elevation, aspect, and slope), and climate (temperature and precipitation). The final model explained 59% of the observed variability in SPI. GIS layers representing SPI for each forest type, along with associated estimates of the prediction variance are developed. Chapter 3 characterizes plant species richness on four major transects in Elba protected area in Egypt. Species data recorded on 63 sample plots were used to characterize the plant species richness by species group (trees, shrubs and subshrubs). Poisson regression was used to identify explanatory variables for estimating species richness of each species group. Important variables included the location of the line transect (A, B, C, and D), soil texture (gravel, sand, silt and clay), pH, and elevation. The final model explained 23%, 58%, and 52% in the variability of species richness for shrubs, subshrubs, and trees, respectively. The results of the study will contribute to the development of an inventory and monitoring program aimed at the conservation and management of species diversity in Elba protected area of Egypt.
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    The relationship between measured soil properties, site-specific management zones, and bare soil reflectance: Colorado, USA
    (Colorado State University. Libraries, 2002) Mzuku, Monga, author; Smith, Freeman Minson, advisor; Khosla, Rajiv, advisor; MacDonald, Lee H., committee member; Reich, Robin Michael, committee member
    Soil productivity varies across farm fields and it is influenced by soil physical and chemical properties. Bare soil imagery can be used to delineate areas of homogeneous soil characteristics, based on variations in reflectance. The objectives of this study were to: (i) evaluate site-specific management zones on the basis of spatial variability in measured soil properties, and (ii) determine the measured soil properties whose variability could be best explained by remotely sensed bare soil reflectance data. The study was conducted on three irrigated fields near Greeley, Wiggins and Yuma in northeastern Colorado, U.S.A. Each field had previously been sub-divided into three management zones corresponding to areas of high, medium and low levels of productivity. Each field was divided into grid cells of 0.4 ha each, with one sample point per cell. The soil properties measured were bulk density, cone index, surface color, organic carbon, texture, total pore space, sorptivity; and surface water content. Surface bulk density and sand content were inversely related to the productivity level of the management zones at study sites I (Greeley) and II (Wiggins). At these study sites, organic carbon and silt content were directly related to the productivity level of the zones. At study site II, clay content and cone index at the 20 cm depth had a direct and indirect relationship, respectively, with the productivity level of the zones. The amount of variability of soil properties that was explained by the appropriate spectral bands ranged from 35 to 55% at site I (Greeley), 13 to 73% at site II (Wiggins), and 10 to 52% at site III (Yuma). In the test involving both zones and wavelength bands, some soil properties were related to either zones or bands only, while others were related to both bands and zones. The amount of variability of soil properties explained by either zones or bands, or a combination of both, ranged from 11 to 77% in Wiggins and 17 to 56% in Yuma. The variation in some of the measured soil properties explained the variable productivity of the management zones. The variation of some soil properties across a field can be explained by the variability in reflectance observed on bare soil imagery.