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Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Resistance of complex interspecific cotton hybrids to wilt

Gulnoza Toshpulatova, Sayfulla Boboyev, Shaxzoda Muxammadiyeva

Article ID: 2563
Vol 5, Issue 2, 2024

DOI: https://doi.org/10.54517/ama.v5i2.2563
VIEWS - 2238 (Abstract)

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Abstract

In the article, the results of determining the degree of tolerance to wilt disease in the wilt background of hybrid families obtained using the method of interspecies complex hybridization in the cotton plant are analyzed. In the F2–F4 generations of interspecies complex hybrids, a large-scale process of separation according to the sign of tolerance to wilt disease takes place, and among the hybrids, plants that are not damaged by wilt disease, partially damaged plants, and severely damaged plants are separated. With this in mind, it was confirmed that it is possible to isolate lines and varieties with different levels of wilt damage from complex hybrid combinations of interspecies with different genomes by purposefully conducting selection work by the breeder. A number of wilt disease-tolerant families compared to control cultivars have been isolated and are recommended for use as a starting source in genetic selection studies.

Keywords

cotton; variety; hybridization; interspecies complex hybridization; genotype; genome; wilt disease; tolerance; tolerance; family; line

References

1. Abdelraheem A, Zhu Y, Zhang J. Quantitative Trait Locus Mapping for Fusarium Wilt Race 4 Resistance in a Recombinant Inbred Line Population of Pima Cotton (Gossypium Barbadense). Pathogens. 2022; 11(10): 1143. doi: 10.3390/pathogens11101143

2. Abdullaev AA, Salakhutdinov IB, Egamberdiev SSH, et al. Analyses of Fusarium wilt race 3 resistance in Upland cotton (Gossypium hirsutum L). Genetica. 2015; 143(3): 385–392. doi: 10.1007/s10709-015-9837-2

3. Abdurakhmonov IY, Abdukarimov A. Application of Association Mapping to Understanding the Genetic Diversity of Plant Germplasm Resources. International Journal of Plant Genomics. 2008; 2008: 1–18. doi: 10.1155/2008/574927

4. Abdurakhmonov IY. Cotton genomics and transgenomics in Uzbekistan. In: Proceedings of the International Cotton Genome Initiative Research Conference; 9–13 October 2012; Raleigh, NC, USA. pp. 1133–1142.

5. Abdullaev AA. Cotton introduction in Uzbekistan—History and perspectives of using of plant introduction problems and perspectives. In: Proceedings of 4th National scientific-applied conference; 3–4 July 2009; Tashkent, Uzbekistan. pp. 59–61.

6. Abdullaev A. Cotton Germplasm Collection of Uzbekistan. The Asian and Australasian Journal of Plant Science and Biotechnology. 2013; 7: 1–15.

7. Abdurakhmonov IY. Evaluation of G. hirsutum exotic accessions from Uzbek cotton germplasm collection for further molecular mapping purposes. In: Proceedings of 2004 Beltwide Cotton Conference; 5–9 January 2004; San Antonio, Texas, USA. pp. 1133–1142.

8. Kappelman AJ. Fusarium wilt resistance in commercial cotton varieties. Plant Disease Reporter. 1971; 52: 896–899.

9. Kappelman AJ. Fusarium wilt resistance in cotton (Gossypium hirsutum). Plant Disease Reporter. 1975; 59: 803–805.

10. Smith SN, DeVay JE, Hsieh WH, et al. Soil-borne populations of Fusarium oxysporum f. sp. vasinfectum, a cotton wilt fungus in California fields. Mycologia. 2001; 93(4): 737–743. doi: 10.1080/00275514.2001.12063205

11. Solovyov AI. Wilt of Cotton [PhD thesis]. State Agricultural Publishing House of the UzSSR; 1954. pp. 18–22.

12. Sidorova SF. Cotton wilting. The spread of agricultural diseases in the USSR in 1968–1972. MINISTER. 1973; 61–66.

13. Marupov A. Fusarium disease of cotton. Agrarian Science. 2012; 22: 141–152.

14. Allen SJ. The effects of crop rotation with wheat on the incidence of Fusarium wilt of cotton in Australia. In: Proceedings of the 2006 Beltwide Cotton Conferences; 3–6 January 2006; San Antonio, Texas, USA. pp. 81–84.

15. Allen SJ. Eliminating seed-borne inoculum of Fusarium oxysporum f. sp. vasinfectum in cotton. In: Proceedings of the 2001 Beltwide Cotton Conferences; 9–13 January 2001; Anaheim, CA, USA. pp. 139–140.

16. Allen SJ. Integrated disease management for Fusarium wilt of cotton in Australia. In: Proceedings of the World Cotton Research Conference; 13 September 2007; Narrabri, Australia. pp. 511–514.

17. Allen SJ. Delayed planting as a control strategy for Fusarium wilt of cotton in Australia. In: Proceedings of the 2005 Beltwide Cotton Conferences; 4–7 January 2005; New Orleans, Louisiana, USA. pp. 131–135.

18. Scheikowski L. Inoculation of rotation crops with Fusarium oxysporum f. sp. vasinfectum. In: Proceedings of the Beltwide Cotton Conferences; 4–7 January 2010; New Orleans, LA, USA. pp. 4–7.

19. Smith SN. Persistence of Fusarium oxysporum f. sp. vasinfectum in Fields in the Absence of Cotton. Phytopathology. 1975; 65(2): 190. doi: 10.1094/phyto-65-190

20. Wang B, Dale ML, Kochman JK, et al. Variations in soil population of Fusarium oxysporum f. sp. vasinfectum as influenced by fertilizer application and growth of different crops. Australasian Plant Pathology. 1999; 28(2): 174. doi: 10.1071/ap99029

21. Kaul S. Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. Nature. 2000; 408(6814): 796–815. doi: 10.1038/35048692

22. Armstrong GM. American, Egyptian, and Indian cotton-wilt fusaria their pathogenicity and relationship to other wilt fusaria. US Dep Agric Tech Bull. 1960; 1219: 1–19.

23. Bennett RS. Method for rapid production of Fusarium oxysporum f. sp. vasinfectum chlamydospores. The Journal of Cotton Science. 2013; 17: 52–59.

24. Bennett RS. Sequence characterization of race 4-like isolates of Fusarium oxysporum from Alabama and Mississippi. The Journal of Cotton Science. 2013; 17: 125–130.

25. Li CQ, Liu GS, Zhao HH, et al. Marker-assisted selection of Verticillium wilt resistance in progeny populations of upland cotton derived from mass selection-mass crossing. Euphytica. 2013; 191(3): 469–480. doi: 10.1007/s10681-013-0917-z

26. Egamberdiev SS, Ulloa M, Saha S, et al. Molecular Characterization of Uzbekistan Isolates of Fusarium oxysporum f. sp. vasinfectum. Journal of Plant Science and Molecular Breeding. 2013; 2(1): 3. doi: 10.7243/2050-2389-2-3

27. Hillocks RJ. Fusarium wilt. In: Hillocks RJ (editor). Cotton Diseases. CAB International, Wallingford; 1992. pp. 127–160.

28. Kim Y, Hutmacher RB, Davis RM. Characterization of California Isolates of Fusarium oxysporum f. sp. vasinfectum. Plant Disease. 2005; 89(4): 366–372. doi: 10.1094/pd-89-0366

29. Wang B, Brubaker CL, Summerell BA, et al. Local origin of two vegetative compatibility groups of Fusarium oxysporumf. sp. vasinfectum in Australia. Evolutionary Applications. 2010; 3(5–6): 505–524. doi: 10.1111/j.1752-4571.2010.00139.x

30. Wang B, Dale ML, Kochman JK, et al. Effects of plant residue, soil characteristics, cotton cultivars and other crops on fusarium wilt of cotton in Australia. Australian Journal of Experimental Agriculture. 1999; 39(2): 203. doi: 10.1071/ea98083

31. Boboev SG. Muratov G. Interspecies hybridization of cotton. Eurasian Union of Scientists (ESU); 2020.

32. Mirakhmedov SM. Development of a method for breeding wilt-resistant cotton varieties and their introduction into production V.sb. Cotton genetics. MINISTER Tashkent, Uzbekistan; 1972. pp. 89–97.

33. Namozov SE, Egamberdiev A, Siddikov A. Inheritance of some horticultural traits in simple and double hybrids of cotton. Bulletin of Uzbekistan Agrarian Science. 2001; 6(4): 55–58.

34. Narayanan SS, Santhanam V, Intergenomic hybridization in Gossypium for the improvement of cultivated allotetraploid cottons. In: Proceedings of the Fourth International Cotton Genome Initiative (ICGI) Workshop; 10–13 October 2004; Hyderabad, India. pp. 113–114.

35. Cronn RC, Small RL, Haselkorn T, et al. Rapid diversification of the cotton genus (Gossypium: Malvaceae) revealed by analysis of sixteen nuclear and chloroplast genes. American Journal of Botany. 2002; 89(4): 707–725. doi: 10.3732/ajb.89.4.707.

36. Saha S, Raska DA, Stelly DM. Upland Cotton (Gossypium hirsutum L.) x Hawaiian Cotton (G. tomentosum Nutt. ex Seem.) F1 Hybrid Hypoaneuploid Chromosome Substitution Series. Journal of Cotton Science. 2006; 10(3): 263–272.

37. Dospehov BA. Methodology of field experience with the basics of statistical processing of research results, 5th ed. Rev. and add M.: Agropromizdat; 1985. p. 416.

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Copyright (c) 2024 Gulnoza Toshpulatova, Sayfulla Boboyev, Shaxzoda Muxammadiyeva

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Zhejiang University, China

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Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

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