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

Potassium deficiency limites reproductive success by altering carbohydrate and protein balances in cotton (Gossypium hirsutum L.)

Reproductive success in higher plants requires a lot of energy and substance provided by carbohydrate and protein metabolism, and potassium (K) plays an important role in carbohydrate and protein metabolism. However, it is unclear whether K deficiency limits reproductive success by disturbing carbohydrate and protein metabolism. The objectives of this study were to explore the effects of K deficiency on carbohydrate and protein metabolism in subtending leaves, phloem and pistils, and their relationship with reproductive success. A cotton cultivar DP0912 was grown in K-deficient (0 mM K+) and K-sufficient (6 mM K+) nutrient solution in growth chambers. Results showed that Pn of the subtending leaves was decreased under K deficiency, but sucrose, starch and free amino acid contents were markedly increased in the K-deficient leaves, because K deficiency limited the translocation of sucrose and amino acid in phloem. As a result, sucrose and free amino acid contents were reduced by 47.3% and 51.8% in the K-deficient pistils than K-sufficient pistils, which led to further decreases in starch and protein accumulation in the K-deficient pistils. Glucose content was also reduced by 53.1% in the K-deficient pistils than K-sufficient pistils, due to the decreased acid and alkaline invertase activities, since sucrose synthase activity was not affected. Lastly, soluble carbohydrate and ATP contents were lower in the K-deficient pistils than K-sufficient pistils, similarly to the changes of pollen tube growth rate and seed set efficiency. It was concluded that the lower carbohydrate and ATP contents in the K-deficient pistils could not meet the energy requirements of pollen tube growth and seed set. Moreover, protein imbalance also limited pollen tube growth. Those changes resulted in lower seed set efficiency to limit reproductive successauthorsversionPeer reviewe

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

Potassium: A Vital Macronutrient in Potato Production—A Review

Potassium (K) is a primary macronutrient for overall plant growth, yield potential, product quality and stress resistance of crops. Potato (Solanum tuberosum L.) crops require a high amount of potassium to achieve the ideal yield and quality. Therefore, the determination of optimum K rate and efficient source for potato is necessary because K affects crop physiological processes, dry matter production, cooking, and processing requirements. Through modeling on the pooled data extracted from 62 studies, the highest tuber yields might be obtained at an exchangeable soil K level of 200 mg kg−1 approximately, dependent on soil pH, texture, and organic matter. Through modeling on the data of 48 studies, it also revealed that application of potassium sulfate (K2SO4) and potassium chloride (KCl) at rates of 200 kg ha−1 and potassium nitrate (KNO3) at a rate of 100 kg ha−1 might achieve the ideal yield, implying the importance of K sources in potato production. However, these values (either soil exchangeable K content, or fertilizer rates) might not be applicable in a specific growing environment for a specific potato variety. It seems that there is no discrimination among split, pre-plant or in-season application of K, although pre-plant fertilization might be a trustworthy strategy for economic tuber yield. Owing to the luxury consumption of K by potato crop, a combination of factors, including soil exchangeable K level, petiole K concentration, crop removal amount, soil conditions, management practices, climatic conditions, and potato variety, should be considered in order to make rational K fertilizer recommendations

Evaluating Digital Agriculture Recommendations with Causal Inference

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. Even though recent advancements in AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. 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 (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability against a changing climate. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results revealed that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities