Impact of Plant Spacing and Nitrogen Rates on Growth Characteristics and Yield Attributes of Egyptian Cotton (Gossypium barbadense L.)

Mineral nutrition for Cannabis sativa in the vegetative stage using response surface analysis

Cannabis cultivated for medical and adult use is a high-value horticultural crop in North America; however, we lack information on its optimal mineral nutrition due to previous legal restrictions. This study evaluated the mineral requirements of nitrogen (N), phosphorus (P), and potassium (K) for cannabis in the vegetative stage using response surface analysis. Plants were cultivated in a hydroponic system with various nutrient solution treatments (mg L-1) of N (132.7, 160, 200, 240, and 267.3), P (9.6, 30, 60, 90, and 110.5), and K (20.8, 60, 117.5, 175, and 214.2) according to a central composite design. Nutrient interactions (N × K, K × P, and N × P × K) had a significant effect on the vegetative growth of the cannabis plants. N × K interaction had a significant effect on leaf mass and stem mass. K × P interaction had a significant effect on dry root mass, leaf mass, stem mass, leaf area, specific leaf area, and chlorophyll a and b contents. N × P × K interaction had a significant effect on root mass, leaf mass, stem mass, stem diameter, leaf area, and chlorophyll a and b contents. The optimum concentrations of total nitrogen, P, K, calcium, and sulfur in the cannabis leaves were 0.54, 0.073, 0.27, 0.56, and 0.38 mg g-1, respectively. An increase in P and K concentrations decreased the magnesium concentration in the leaves, but it was unaffected by the increase in N concentration. The recommended primary macronutrients for cannabis plants in the vegetative stage based on the maximum desirability and nutrient use efficiencies were 160-200 mg L-1 N, 30 mg L-1 P, and 60 mg L-1 K. These findings can offer valuable insight and guidance to growers regarding the mineral requirements for cannabis during the vegetative stage.

Exploring agro-morphological and fiber traits diversity in cotton (G. barbadense L.)

Cotton (Gossypium barbadense L.) is a leading fiber and oilseed crop globally, but genetic diversity among breeding materials is often limited. This study analyzed genetic variability in 14 cotton genotypes from Egypt and other countries, including both cultivated varieties and wild types, using agro-morphological traits and genomic SSR markers. Field experiments were conducted over two seasons to evaluate 12 key traits related to plant growth, yield components, and fiber quality. Molecular diversity analysis utilized 10 SSR primers to generate DNA profiles. The Molecular diversity analysis utilized 10 SSR primers to generate DNA profiles. Data showed wide variation for the morphological traits, with Egyptian genotypes generally exhibiting higher means for vegetative growth and yield parameters. The top-performing genotypes for yield were Giza 96, Giza 94, and Big Black Boll genotypes, while Giza 96, Giza 92, and Giza 70 ranked highest for fiber length, strength, and fineness. In contrast, molecular profiles were highly polymorphic across all genotypes, including 82.5% polymorphic bands out of 212. Polymorphism information content was high for the SSR markers, ranging from 0.76 to 0.86. Genetic similarity coefficients based on the SSR data varied extensively from 0.58 to 0.91, and cluster analysis separated genotypes into two major groups according to geographical origin. The cotton genotypes displayed high diversity in morphology and genetics, indicating sufficient variability in the germplasm. The combined use of physical traits and molecular markers gave a thorough understanding of the genetic diversity and relationships between Egyptian and global cotton varieties. The SSR markers effectively profiled the genotypes and can help select ideal parents for enhancing cotton through hybridization and marker-assisted breeding.

Genetic improvement of Egyptian cotton (Gossypium barbadense L.) for high yield and fiber quality properties under semi arid conditions

Between 2016 and 2018, the Agriculture Research Center's Sakha Agriculture Research Station conducted two rounds of pedigree selection on a segregating population of cotton (Gossypium barbadense L.) using the F2, F3, and F4 generations resulting from crossing Giza 94 and Suvin. In 2016, the top 5% of plants from the F2 population were selected based on specific criteria. The superior families from the F3 generation were then selected to produce the F4 families in 2017, which were grown in the 2018 summer season in single plant progeny rows and bulk experiments with a randomized complete block design of three replications. Over time, most traits showed increased mean values in the population, with the F2 generation having higher Genotypic Coefficient of Variance (GCV) and Phenotypic Coefficient of Variance (PCV) values compared to the succeeding generations for the studied traits. The magnitude of GCV and PCV in the F3 and F4 generations was similar, indicating that genotype had played a greater role than the environment. Moreover, the mean values of heritability in the broad sense increased from generation to generation. Selection criteria I2, I4, and I5 were effective in improving most of the yield and its component traits, while selection criterion I1 was efficient in improving earliness traits. Most of the yield and its component traits showed a positive and significant correlation with each other, highlighting their importance in cotton yield. This suggests that selecting to improveone or more of these traits would improve the others. Families number 9, 13, 19, 20, and 21 were the best genotypes for relevant yield characters, surpassing the better parent, check variety, and giving the best values for most characters. Therefore, the breeder could continue to use these families in further generations as breeding genotypes to develop varieties with high yields and its components.

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R² in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R² increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.