Effect of Water-deficit Stress on Cotton During Reproductive Development

Water deficit is a major abiotic factor limiting plant growth and crop productivity around the world. Cotton (Gossypium hirsutum L.) is considered to be relatively tolerant to drought and the effects of water stress on leaf physiology and metabolism have been extensively documented. However, information is lacking on the effect of water-deficit stress on the cotton flower. It was hypothesized that water-deficit stress would impair gas exchange functions which consequently would result in perturbation of carbohydrates of cotton reproductive units. To investigate this hypothesis growth room studies and field studies were conducted with the objectives being to document the physiological and biochemical changes that take place in cotton flowers and their subtending leaves when subjected to limited water supply. Additionally, the effect of the ethylene inhibitor 1-Methylcyclopropene under conditions of water stress was investigated as well as the response of leaf and ovary polyamine metabolism of two cotton cultivars differing in drought tolerance. Results indicated that water-deficit stress during flowering significantly compromised leaf gas exchange functions resulting in decreased stomatal conductance, photosynthesis, respiration and water potential. However, cotton reproductive units appeared to be less drought-sensitive compared to the leaves possibly due to higher water potential and glutathione reductase activity. Limited supply of water significantly affected carbohydrate metabolism of both leaf and pistil resulting in carbohydrate accumulation. Contrary to expectations, application of the ethylene inhibitor 1-MCP had no effect on leaf gas exchange function, however, it reversed the effect of water stress on pistil sucrose concentrations. Finally, water-deficit stress during flowering had a significant effect on polyamine metabolism of both leaf and pistil, resulting in increases in putrescine, spermidine and spermine in drought-sensitive cultivars. The differential response of polyamine metabolism between drought-sensitive and tolerant cultivars suggests that polyamines could be effective tools not only in selection of drought-tolerant cultivars, but also in drought tolerance engineering, however further research is needed in order to elucidate the exact pathways of their action

Comparison of growing media for container grown plants

Greenhouse and growth chamber experiments are conducted worldwide in efforts to produce solutions that would increase yields of agronomic crops. However, the results of those experiments vary due to the many growth media being used. An experiment was conducted in the fall of 2010 to identify a broadly acceptable growth media that would produce uniform stands and optimum results in greenhouse and growth chamber settings. A total of six growth media were tested on cotton (Gossypium hirsutum) at the Arkansas Agricutural Research and Extension Center’s Altheimer Lab in Fayetteville. The plants grown in each medium were harvested six weeks after planting and the measurements performed included plant height, plant dry matter, leaf area, and nutrient analysis. The results indicated that a positive, significant difference (P \u3c 0.05) existed between “Sunshine” mix (MIX1) and the other media. Plants grown in MIX1 experienced greater plant height, dry matter, leaf area, and also experienced higher leaf tissue levels of N, P, and S. “Sunshine” (Mix1) is a readily available growth medium that produces optimum plant growth and uniform results in growth chamber and greenhouse experiments

The influence of poultry litter biochar on early season cotton growth

Cotton is known for being sensitive to cool, wet soils, especially in the early stages of growth. Amendments to soil can aid cotton seedlings in development and nutrient uptake. However, soil amendments can be costly and detrimental to the environment, and alternatives such as the addition of biochar have been considered. Biochar is produced from biomass that has gone through pyrolysis and has been shown to improve plant yield, microbial response, soil structure, soil cation–exchange capacity, and water use efficiency. This study was conducted to evaluate the effect of biochar on early season cotton growth. The aim of this study was to determine whether biochar aids nutrient uptake and seedling development during the seedling’s life cycle. The study was established in October 2013 in the greenhouse at the University of Arkansas using a randomized complete block design with three replications. Treatments included a control with no fertilizer or biochar, a control with fertilizer (56 kg N/ ha) and no biochar, and two fertilizer treatments (0 or 56 kg N/ ha) each with 1500 or 3000 kg/ha biochar. Plants were grown for eight weeks then harvested to collect plant height, plant fresh weight, plant dry weight, and leaf area. Data showed that the highest level of biochar with additional fertilizer provided the best growth response in plant height, fresh weight dry weight, and leaf area at 27.52 cm, 14.7g, 1.87 g, and 419.48 cm2 , respectively

Evaluating Digital Tools for Sustainable Agriculture using Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, it lacks tangible quantitative evidence on its benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust by enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop a causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently estimate it using several methods on observational data. The results show that a field sown according to our recommendations enjoyed a significant increase in yield (12% to 17%).Comment: Accepted for publication and spotlight presentation at Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

Impacts of abiotic stresses on the physiology and metabolism of cool-season grasses:A review

Grasslands cover more than 70% of the world's agricultural land playing a pivotal role in global food security, economy, and ecology due to their flexibility and functionality. Climate change, characterized by changes in temperature and precipitation patterns, and by increased levels of greenhouse gases in the atmosphere, is anticipated to increase both the frequency and severity of extreme weather events, such as drought, heat waves, and flooding. Potentially, climate change could severely compromise future forage crop production and should be considered a direct threat to food security. This review aimed to summarize our current understanding of the physiological and metabolic responses of temperate grasses to those abiotic stresses associated with climate change. Primarily, substantial decreases in photosynthetic rates of cool‐season grasses occur as a result of high temperatures, water‐deficit or water‐excess, and elevated ozone, but not CO2 concentrations. Those decreases are usually attributed to stomatal and non‐stomatal limitations. Additionally, while membrane instability and reactive oxygen species production was a common feature of the abiotic stress response, total antioxidant capacity showed a stress‐specific response. Furthermore, climate change‐related stresses altered carbohydrate partitioning, with implications for biomass production. While water‐deficit stress, increased CO2, and ozone concentrations resulted in higher carbohydrate content, the opposite occurred under conditions of heat stress and flooding. The extent of damage is greatly dependent on location, as well as the type and intensity of stress. Fortunately, temperate forage grass species are highly heterogeneous. Consequently, through intra‐ and in particular inter‐specific plant hybridization (e.g., Festuca x Lolium hybrids) new opportunities are available to harness, within single genotypes, gene combinations capable of combating climate change

Deriving Forest Fire Probability Maps From the Fusion of Visible/Infrared Satellite Data and Geospatial Data Mining

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post- fire conditions. The GIS-based modeling was based on a Multi Criterion Evaluation (MCE) technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices

Deriving Forest Fire Probability Maps From the Fusion of Visible/Infrared Satellite Data and Geospatial Data Mining

Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and robability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices