Effects of Interspecific Chromosome Substitution in Upland Cotton on Cottonseed Micronutrients

Genetics, Breeding and Genetic Engineering to Improve Cottonseed Oil and Protein: A Review

Upland cotton (Gossypium hirsutum) is the world's leading fiber crop and one of the most important oilseed crops. Genetic improvement of cotton has primarily focused on fiber yield and quality. However, there is an increased interest and demand for enhanced cottonseed traits, including protein, oil, fatty acids, and amino acids for broad food, feed and biofuel applications. As a byproduct of cotton production, cottonseed is an important source of edible oil in many countries and could also be a vital source of protein for human consumption. The focus of cotton breeding on high yield and better fiber quality has substantially reduced the natural genetic variation available for effective cottonseed quality improvement within Upland cotton. However, genetic variation in cottonseed oil and protein content exists within the genus of Gossypium and cultivated cotton. A plethora of genes and quantitative trait loci (QTLs) (associated with cottonseed oil, fatty acids, protein and amino acids) have been identified, providing important information for genetic improvement of cottonseed quality. Genetic engineering in cotton through RNA interference and insertions of additional genes of other genetic sources, in addition to the more recent development of genome editing technology has achieved considerable progress in altering the relative levels of protein, oil, fatty acid profile, and amino acids composition in cottonseed for enhanced nutritional value and expanded industrial applications. The objective of this review is to summarize and discuss the cottonseed oil biosynthetic pathway and major genes involved, genetic basis of cottonseed oil and protein content, genetic engineering, genome editing through CRISPR/Cas9, and QTLs associated with quantity and quality enhancement of cottonseed oil and protein.

Change in cotton plant quality in response to application of anaerobically digested sewage sludge

Treated municipal sewage sludge (TSS) was applied to the cotton plant at rates of 10, 20 and 30 t/ha per year. Seed cotton yield (71.4%), lint yield (67.7%) and cottonseed yield (74.1%) were increased significantly when sludge was applied at a rate of 30 t/ha (TSS3). The effects of TSS applications on seed yield, lint yield and cottonseed yield were listed as TSS3 > TSS2 > CF > TSS1 > C according to the applications. The increasing TSS levels had a positive effect and increased the total N concentration compared to the control. The highest N value was observed in TSS3 plots, while the lowest value was recorded in control (C) plots. The highest P value was found in control (C) at 0.80% and in 10 t/ha (TSS1) at 0.80%, while the lowest value was found in the TSS2 application (0.70%). The K concentration of cottonseed increased with the increasing TSS rates, from 1.56% in control plots to 2.20% in 20 t/ha application (TSS2). Corresponding to the TSS levels, the calcium of plant tissues was determined by a range of 0.12–0.13%. The treatments of TSS and mineral fertilizer had similar effects on the Mg content of cottonseed, which was in the range of 0.38–0.43%. Na content in plant tissue increased with increasing dose of sludge application compared to control soils. Increasing doses of TSS had no significant effect on the concentrations of iron (Fe), copper (Cu), zinc (Zn), manganese (Mn) and boron (B) in cottonseed. The order of the elements with respect to their amounts in cottonseed was as follows: Fe > Zn > Na > B > Mn > Cu. The concentrations of non-essential elements (Ni, Cd, Cr, Pb, Hg and As) in cottonseed were below the permissible limits.

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.