No description available.

No description available.

No description available.

Crop emergence is a major factor limiting crop yield, especially in hot climates where soil dries quickly after rainfall or irrigation. Problems with the emergence of cotton in Pakistan are of particular importance because of the high value of the crop and its contribution to national economy. A complex interaction of factors involving climate, seed properties, soil physical properties and soil management determine crop emergence and hence establishment. This means modelling of emergence is an important way of determining the combination of conditions at which emergence becomes limited. The studies reported here show the effect of temperature, matric potential and mechanical impedance on pre-emergent root and shoot growth of cotton variety MNH-147. The effect of osmotic potential and temperature on time to germination and cumulative germination of cotton is also described with some preliminary work on wheat. Finally a small field experiment was performed in Pakistan to identify major factors limiting emergence and provide data for future validation of a computer model of emergence. Time to germination was found to be a function of temperature and metric potential. It reduced with increase in temperature and osmotic potential. A linear relationship between temperature and germination rate (1/time to germination) indicated a base temperature of 9.8 °C. Germination rate also decreased linearly with decreasing osmotic potential between zero and -500 kPa. Thus the concept of hydrothermal time can be used to model germination and parameters to fit this model were determined. Root and shoot lengths of pre-emergent cotton seedling increased with increase in temperature from 22 to 32 °C but were reduced with a further increase to 38 °C. At any temperature, lengths increased linearly with time at a rate controlled by temperature. During the first 192 h after germination, growth was divided into two distinct phases: a linear increase with time followed by no further growth.

This dissertation investigates: (1) the climate change effects on the mean and higher order moments of crop yield distributions; (2) the effects of irrigation with and without its interactive terms with climate variables; (3) the climate effects on crop mix and climate change adaptation. The first essay explores how the climate change impacts the crop yield distribution. Using the flexible moment based approach, this study infers that external climate factors influence not only mean crop yield and variability, but also its higher order moments, skewness and kurtosis. The climate effects on each moment vary by crops. The second essay examines the irrigation effects on the mean crop yield. While the irrigation effects estimated from the model with irrigation dummy are constant regardless of climate conditions, the irrigation effects estimated from the model with irrigation dummy and interactive variables between irrigation and climate are affected by external climate factors. This study shows that as temperature increases, the irrigation effects are decreased and irrigation reduces damages from extreme temperature conditions. Precipitation and PDSI effects are also diminished under irrigation. The third essay explores the effects of climate on crop producers' choice. Our findings point out that the climate factors have significant impacts on crop choice and future climate change will alter the crop mix. Under the projected climate change of increasing temperature and precipitation, wheat and soybeans cropland will be switched to upland cotton. The major producing locations of upland cotton, rice, and soybeans will be shifted to the north. However, most of corn will be still cultivated in the Corn Belt and changes in acreage planted will not be significant.

Thesis (Ph.D.)--University of Adelaide, Dept. of Agronomy & Farming Systems, 1997? / Bibliography: leaves 155-169.

Over one billion people, more than 1/9th of the global population, are undernourished. Feeding the ever increasing population has to be the most important goal of plant sciences. Since cultivated areas are not likely to increase, I will need to produce more with what is available. This can be summarized in one word: yield. Unfortunately, wheat’s yield is expected to increase only 1.13% by 2019, a prediction that if converted into reality will likely indicate that I failed to cope with the world demographic increase. A new strategy to revolutionize wheat production is required, and some believe that this change might be represented by wheat hybrids. Achieving adequate commercial production of wheat hybrids has the potential to nearly double the yield of one of the world’s most important staple food. The first fundamental step toward this goal is to develop feasible methodologies to sterilize the male part of the complete wheat flowers. Two fertility-related genes are the primary target of this study, namely the species cytoplasm specific on chromosome 1D, and the desynaptic locus on chromosome 3B. This dissertation summarizes the important achievements obtained toward the cloning of the two loci by means of radiation hybrid functional analysis. Radiation hybrid is a technique that employs radiation to create genetic diversity along the targeted chromosome. Chapter 1 explains in details how this methodology can be applied to plants. The use of radiation hybrid mapping permitted creating a comprehensive map of wheat chromosome 3B, as discussed in Chapter 2, and then expanded the mapping information to identify the 2 Mb location of the desynaptic locus desw2, as discussed in Chapter 3. A similar approach on chromosome 1D allowed first to pinpoint the location of the species cytoplasm specific gene to a region of 2 Mb, as discussed in Chapter 4, and then ultimately to find a strong candidate for this locus, as discussed in Chapter 5. Now that the molecular locations of these genes have been unraveled by this study, their sequence can be streamlined into transformation to ultimately produce female wheat plants, and consequently hybrids.

The success of a breeding program depends upon the ability of the plant breeder to not only develop new lines, but also to determine which lines or varieties are superior. For this purpose, yield trials are an important yardstick for determining the value of the trial in assessing the merits of the different lines. Variation encountered is due to several factors; with true genetic differences, environment, and human error being of major importance. In order for yield trials to give meaningful information on a line or variety, it is essential that variation due to human error and environment be reduced to a minimum. Uniform space planting, careful cultivation, and irrigation can help reduce the human error. However, the effect of environment, mainly soil heterogeneity, is not so easily handled. Randomization, replication, and planting experimental plots of the proper shape with sufficient size to give the desired information are techniques devised to minimize the effect of soil heterogeneity. In general, it has been found that the larger the experimental plots, the smaller the variation (1, 23). Also, plots with a large length to width ratio lying in the direction of greatest soil variability have been found to decrease the variability of the yield trial (2, 8, 32, 35). However, both expense and convenience will modify the size and shape of plot that is desired. The optimum size and shape of a plot would be one that gives a maximum amount of information at a minimum cost and be of convenient handling dimensions. Such information must be determined for each individual crop.

This research uncovers a novel pathway in precision agriculture, emphasizing the utilization of advanced supervised and self-supervised deep learning approaches for an innovative solution to weed detection and crop yield prediction. The study focuses on key weed species: Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, which are troublesome weeds in the United States. One of the most innovative components of this research is the debut of a self-supervised learning approach specifically tailored for soybean yield prediction using only unlabeled RGB images. This novel strategy presents a departure from traditional yield prediction methods that consider multiple variables, thus offering a more streamlined and efficient methodology that presents a significant contribution to the field. To address the monitoring of Italian ryegrass in wheat cultivation, a bespoke Convolutional Neural Network (CNN) model was developed. It demonstrated impressive precision and recall rates of 100% and 97.5% respectively, in accurately classifying Italian ryegrass in the wheat. Among three hyperparameter tuning methods, Bayesian optimization emerges as the most efficient, delivering optimal results in just 10 iterations, contrasting with 723 and 304 iterations required for grid search and random search respectively. Further, this study examines the performance of various classification and object detection algorithms on Unmanned Aerial Systems (UAS)-acquired images at different growth stages of soybean and Palmer amaranth. Both the Vision Transformer and EfficientNetB0 models display promising test accuracies of 97.69% and 93.26% respectively. However, considering a balance between speed and accuracy, YOLOv6s emerged as the most suitable object detection model for real-time deployment, achieving an 82.6% mean average precision (mAP) at an average inference speed of 8.28 milliseconds. Furthermore, a self-supervised contrastive learning approach was introduced for automating the labeling of Palmer amaranth and soybean. This method achieved a notable 98.5% test accuracy, indicating the potential for cost-efficient data acquisition and labeling to advance precision agriculture research. A separate study was conducted to detect common ragweed in soybean crops and the prediction of soybean yield impacted by varying weed densities. The Vision Transformer and MLP-Mixer models achieve test accuracies of 97.95% and 96.92% for weed detection, with YOLOv6 outperforming YOLOv5, attaining an mAP of 81.5% at an average inference speed of 7.05 milliseconds. Self-supervised learning-based yield prediction models reach a coefficient of determination of up to 0.80 and a correlation coefficient of 0.88 between predicted and actual yield. In conclusion, this research elucidates the transformative potential of self-supervised and supervised deep learning techniques in revolutionizing weed detection and crop yield prediction practices. Its findings significantly contribute to precision agriculture, paving the way for efficient and cost-effective site-specific weed management strategies. This, in turn, promotes reduced environmental impact and enhances the economic sustainability of farming operations. / Master of Science in Life Sciences / This novel research provides a fresh approach to overcoming some of the biggest challenges in modern agriculture by leveraging the power of advanced artificial intelligence (AI) techniques. The study targets key disruptive weed species, such as, Italian ryegrass in wheat, Palmer amaranth, and common ragweed in soybean, all of which have the potential to significantly reduce crop yields. The studies were first conducted to detect Italian ryegrass in wheat crops, utilizing RGB images. A model is built using a complex AI system called a Convolutional Neural Network (CNN) to detect this weed with remarkable accuracy. The study then delves into the use of drones to take pictures of different growth stages of soybean and Palmer amaranth plants. These images were then analyzed by various AI models to assess their ability to accurately identify the plants. The results show some promising findings, with one model being quick and accurate enough to be potentially used in real-time applications. The most important part of this research is the application of self-supervised learning, which learns to label Palmer amaranth and soybean plants on its own. This novel method achieved impressive test accuracy, suggesting a future where data collection and labeling could be done more cost-effectively. In another related study, we detected common ragweed in soybean crops and predicted soybean yield based on various weed densities. AI models once again performed well for weed detection and yield prediction tasks, with self-supervised models showcasing high agreement between predicted and actual yields. In conclusion, this research showcases the exciting potential of self-teaching and supervised AI in transforming the way we detect weeds and predict crop yields. These findings could potentially lead to more efficient and cost-effective ways of managing weeds at specific sites. This could have a positive impact on the environment and improve the economic sustainability of farming operations, paving the way for a greener future.

The purpose of this investigation was to determine if a commercial soil conditioner, Superbio, can improve crop yield, and if the "advertised" soil improvement might be due to an increase in the activity and numbers of aerobic cellulose decomposing bacteria following treatment.