Modern Concepts and Techniques for Better Cotton Production

  • Abdul Ghaffar
  • Muhammad Habib ur RahmanEmail author
  • Hafiz Rizwan Ali
  • Ghulam Haider
  • Saeed Ahmad
  • Shah Fahad
  • Shakeel Ahmad


Sustainable cotton production in current environmental conditions is under threat due to climatic variability and shortage of ever-decreasing resources for agricultural crops. There is dire need to improve the cotton production to fulfill increasing demands of the ever increasing world population which will rise up to nine billion till 2050. Poor soil health, poor water quality and water shortage, insect pest complex, and unpredictable climatic patterns are predominant problems to cotton production. Hence, there is a great challenge to manage cotton crop in a sustainable fashion without the degradation of soil, water, and environment due to climate variability. There are several factors associated with low production of cotton including improper sowing and picking, poor pesticide spraying approaches, inappropriate amount and time of irrigation, processing and ginning through inappropriate and primitive procedures, heat stress, lack of disease- and pest-tolerant varieties, improper nutrient management, improper disease management, and improper weed management. It is the need of the hour to adopt the modern technologies and applications for sustainable cotton production. There are several modern technologies which can increase the production of cotton and make the idea of sustainability feasible because of their site-specific management of all agricultural inputs. GPS, GIS, and remote sensing technologies make the precise seeding of cotton seed, fertilizers, and pesticides. IPM, IWM, and INM are the well-developed modern concepts which not only reduce the cost of production but also mitigate the emission of greenhouse gases. For sustainable cotton production, implementation of these modern concepts is crucial so that the human beings will get benefits in the future. Therefore, this chapter will be focused on the recently developed technologies which can be sustainably utilized for the better management of cotton crop across the world. This chapter will explore the importance of Decision Support system (DSS) for sustainable cotton production; role of GPS, GIS, and remote sensing for identifying site-specific factors such as soil quality indicators; importance of transgenic cotton; impact of mechanical sowing and picking on sustainable cotton production; use of UAVs for nutrient and pesticide management; and impacts of modern concepts on increasing agronomic production and advancing global fiber and oil security.


Sustainable cotton production GIS GPS Remote sensing Fiber security 



Autoregressive integrated moving average


Autoregressive moving average


Cropping system model


Decision support system


Electrical conductivity




Frequency domain reflectometry


Geographic information system


Global positioning system


Global system for mobile communication


Integrated pest management


Information retrieval system


Integrated weed management


Leaf area index


Marker-assisted recurrent selection


Marker-assisted selection


Normalized difference vegetation index


Nuclear magnetic resonance


Precision agriculture


Remote sensing


Seed cotton yield


Surface energy balance algorithm for land


Unmanned aerial vehicle


Variable rate application


Volumetric water content


World Health Organization


Water use efficiency


  1. Abbas Q, Ahmad S (2018) Effect of different sowing times and cultivars on cotton fiber quality under stable cotton-wheat cropping system in southern Punjab, Pakistan. Pak J Life Soc Sci 16:77–84Google Scholar
  2. Abdalla K, Chivenge P, Ciais P, Chaplot V (2016) No-tillage lessens soil CO2 emissions the most under arid and sandy soil conditions: results from a meta-analysis. Biogeosciences 13(12):3619–3633CrossRefGoogle Scholar
  3. Ahmad S, Raza I (2014) Optimization of management practices to improve cotton fiber quality under irrigated arid environment. J Food Agric Environ 2(2):609–613Google Scholar
  4. Ahmad S, Raza I, Ali H, Shahzad AN, Atiq-ur-Rehman, Sarwar N (2014) Response of cotton crop to exogenous application of glycinebetaine under sufficient and scarce water conditions. Braz J Bot 37(4):407–415Google Scholar
  5. Ahmad S, Abbas Q, Abbas G, Fatima Z, Atique-ur-Rehman, Naz S, Younis H, Khan RJ, Nasim W, Habib ur Rehman M, Ahmad A, Rasul G, Khan MA, Hasanuzzaman M (2017) Quantification of climate warming and crop management impacts on cotton phenology. Plants 6(7):1–16Google Scholar
  6. Ahmad S, Iqbal M, Muhammad T, Mehmood A, Ahmad S, Hasanuzzaman M (2018) Cotton productivity enhanced through transplanting and early sowing. Acta Sci Biol Sci 40:e34610Google Scholar
  7. Al Zayed IS, Elagib NA, Ribbe L, Heinrich J (2015) Spatio-temporal performance of large-scale Gezira irrigation scheme, Sudan. Agr Syst 133:131–142CrossRefGoogle Scholar
  8. Ali MH (2011) GIS in irrigation and water management. In: Practices of irrigation & on-farm water management, vol 2. Springer, New York, NYCrossRefGoogle Scholar
  9. Ali H, Afzal MN, Ahmad F, Ahmad S, Akhtar M, Atif R (2011) Effect of sowing dates, plant spacing and nitrogen application on growth and productivity on cotton crop. Int J Sci Eng Res 2(9):1–6Google Scholar
  10. Ali H, Abid SA, Ahmad S, Sarwar N, Arooj M, Mahmood A, Shahzad AN (2013a) Integrated weed management in cotton cultivated in the alternate-furrow planting system. J Food Agric Environ 11(3–4):1664–1669Google Scholar
  11. Ali H, Abid SA, Ahmad S, Sarwar N, Arooj M, Mahmood A, Shahzad AN (2013b) Impact of integrated weed management on flat-sown cotton (Gossypium hirsutum L.). J Anim Plant Sci 23(4):1185–1192Google Scholar
  12. Ali H, Hameed RA, Ahmad S, Shahzad AN, Sarwar N (2014a) Efficacy of different techniques of nitrogen application on American cotton under semi-arid conditions. J Food Agric Environ 12(1):157–160Google Scholar
  13. Ali H, Hussain GS, Hussain S, Shahzad AN, Ahmad S, Javeed HMR, Sarwar N (2014b) Early sowing reduces cotton leaf curl virus occurrence and improves cotton productivity. Cercetări Agronomice în Moldova XLVII(4):71–81Google Scholar
  14. Ali S, Badar N, Fatima H (2015) Forecasting production and yield of sugar cane and cotton crops of Pakistan for 2013-2030. Sarhad J Agric 31(1):1–9Google Scholar
  15. Ali L, Anum W, Hussain G, Shahid MI (2017) Enhancement in cotton (Gossypium hirsutum L.) crop yield by water use efficiency under various planting techniques. Environ Earth Ecol 1(2):6–16CrossRefGoogle Scholar
  16. Altieri M, Nicholls C (2004) Biodiversity and pest management in agroecosystems, 2nd edn. CRC Press, Boca Raton, FL, pp 1–252CrossRefGoogle Scholar
  17. Amin A, Nasim W, Mubeen M, Nadeem M, Ali L, Hammad HM, Sultana SR, Jabran K, Habib ur Rehman M, Ahmad S, Awais M, Rasool A, Fahad S, Saud S, Shah AN, Ihsan Z, Ali S, Bajwa AA, Hakeem KR, Ameen A, Amanullah, Rehman HU, Alghabar F, Jatoi GH, Akram M, Khan A, Islam F, Ata-Ul-Karim ST, Rehmani MIA, Hussain S, Razaq M, Fathi A (2017) Optimizing the phosphorus use in cotton by using CSM-CROPGRO-cotton model for semi-arid climate of Vehari-Punjab, Pakistan. Environ Sci Pollut Res 24(6):5811–5823CrossRefGoogle Scholar
  18. Amin A, Nasim W, Mubeen M, Ahmad A, Nadeem M, Urich P, Fahad S, Ahmad S, Wajid A, Tabassum F, Hammad HM, Sultana SR, Anwar S, Baloch SK, Wahid A, Wilkerson CJ, Hoogenboom G (2018) Simulated CSM-CROPGRO-cotton yield under projected future climate by SimCLIM for southern Punjab, Pakistan. Agr Syst 167:213–222CrossRefGoogle Scholar
  19. Arshad MN, Ahmad A, Wajid SA, Cheema MJM, Schwartz MW (2017) Adapting DSSAT model for simulation of cotton yield for nitrogen levels and planting dates. Agron J 109(6):2639–2648CrossRefGoogle Scholar
  20. Asghar M, Farooq M, Hussain M (2016) Productivity and profitability of cotton – wheat system as influenced by relay intercropping of insect resistant transgenic cotton in bed planted wheat. Eur J Agron 75:33–41CrossRefGoogle Scholar
  21. Ashigh J, Mohseni-Moghadam M, Idowu J, Hamilton C (2015) Weed management in cotton. NMSU, Las Cruces, NMGoogle Scholar
  22. Attia S, Grissa KL, Lognay G, Bitume E, Hance T, Mailleux AC (2013) A review of the major biological approaches to control the worldwide pest Tetranychus urticae (Acari: Tetranychidae) with special reference to natural pesticides. J Pest Sci 86(3):361–386CrossRefGoogle Scholar
  23. Ba HGL, Ai HMT, Wu PE (2008) Preliminary study on biological control of cotton bollworm, Helicoverpa armigera (Hubner) with Trichogramma in Tulufan in Xinjiang autonomous region. China Cotton 35:17–18Google Scholar
  24. Ballester C, Hornbuckle J, Brinkhoff J, Smith J, Quayle W (2017) Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery. Remote Sens (Basel) 9(11):1149CrossRefGoogle Scholar
  25. Bastiaanssen WGM, Ali S (2003) A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric Ecosyst Environ 94(3):321–340CrossRefGoogle Scholar
  26. Bechar A, Nof SY, Wachs JP (2015) A review and framework of laser-based collaboration support. Ann Rev Control 39:30–45CrossRefGoogle Scholar
  27. Benedict JH, Sachs ES, Altman DW, Ring DR, Stone TB, Sims SR (1993) Impact of δ-endotoxin-producing transgenic cotton on insect–plant interactions with Heliothis virescens and Helicoverpa zea (Lepidoptera: Noctuidae). Environ Entomol 22(1):1–9CrossRefGoogle Scholar
  28. Buttar GS, Sidhu HS, Singh V, Jat ML, Gupta R, Singh Y, Singh B (2013) Relay planting of wheat in cotton: an innovative technology for enhancing productivity and profitability of wheat in cotton–wheat production system of South Asia. Exp Agric 49(1):19–30CrossRefGoogle Scholar
  29. Cade-Menun BJ (2017) Characterizing phosphorus forms in cropland soils with solution 31 P-NMR: past studies and future research needs. Chem Biol Technol Agric 4(1):19CrossRefGoogle Scholar
  30. Cade-Menun B, Liu CW (2014) Solution phosphorus-31 nuclear magnetic resonance spectroscopy of soils from 2005 to 2013: a review of sample preparation and experimental parameters. Soil Sci Soc Am J 78(1):19–37CrossRefGoogle Scholar
  31. Campbell B, Chen L, Dygert C, Dick W (2014) Tillage and crop rotation impacts on greenhouse gas fluxes from soil at two long-term agronomic experimental sites in Ohio. J Soil Water Conserv 69(6):543–552CrossRefGoogle Scholar
  32. Cariou C, Lenain R, Thuilot B, Berducat M (2009) Automatic guidance of a four-wheel-steering mobile robot of accurate field operations. J Field Robot 26(6-7):504–518CrossRefGoogle Scholar
  33. Caverzan A, Giacomin R, Müller M, Biazus C, Lângaro NC, Chavarria G (2018) How does seed vigor affect soybean yield components? Agron J 110(4):1318–1327CrossRefGoogle Scholar
  34. Chandel NS, Agrawal KN, Tripathi H, Garg SK (2014) Development of yield maps in wheat using yield monitor. Bhartiya Krishi Anusandhan Patrika 29(3):111–115Google Scholar
  35. Clay SA, Chang J, Clay DE, Reede CL, Dalsted K (2004) Using remote sensing to develop weed management zones in soybeans. Site Spec Manag Guide 42:1–4Google Scholar
  36. Cook SE, Bramley RG (1998) Precision agriculture – opportunities, benefits and pitfalls. Aust J Exp Agric 38:753–763CrossRefGoogle Scholar
  37. Copeland JD, Dodds DM, Catchot AL, Gore J, Wilson DG Jr (2016) Evaluation of PRE herbicides and seed treatment on thrips infestation and cotton growth, development, and yield. Agron J 108(6):2355–2364CrossRefGoogle Scholar
  38. Croft BA, Cook RJ, MacKenzie DR (1985) Biological constraints. In: Gibbs M, Carlson C (eds) Crop productivity--research imperatives revisited: an international conference held at Boyne Highlands Inn, Harbor Springs, Michigan, pp 177–195Google Scholar
  39. Dalezios NR, Domenikiotis C, Loukas A, Tzortzios ST, Kalaitzidis C (2001) Cotton yield estimation based on NOAA/AVHRR produced NDVI. Phys Chem Earth B Hydrol Ocean Atmos 26(3):247–251CrossRefGoogle Scholar
  40. Daughtry DW, Porter WM, Harris GH, Noland RL, Snider JL, Virk S (2018) Correlating plant nitrogen status in cotton with UAV based multispectral imagery. In: A paper in 14th International Conference on Precision Agriculture 24-27 June, 2018, Montreal, Quebec, Canada. ISPA, Monticello, IL, pp 1–9Google Scholar
  41. Dawson A, Knowles O (2018) To grid or not to grid – a review of soil sampling strategies. In: Currie LD, Christensen CL (eds) Farm environmental planning – science, policy and practice. Fertilizer and Lime Research Centre, Massey University, Palmerston North. Available at: Google Scholar
  42. Debnath MK, Bera K, Mishra P (2013) Forecasting area, production and yield of cotton in India using ARIMA model. J Space Sci Technol 2(1):16–20Google Scholar
  43. Deguine JP, Ferron P, Russell D (2008) Sustainable pest management for cotton production. A review. Agron Sustain Dev 28:113–137CrossRefGoogle Scholar
  44. Deshmukh AS, Mohanty A (2016) Cotton mechanisation in India and across globe: a review. Int J Adv Res Eng Sci Technol 3(1):66Google Scholar
  45. Difallah W, Benahmed K, Draoui B, Bounaama F (2017) Linear optimization model for efficient use of irrigation water. Int J Agron 2017:5353648CrossRefGoogle Scholar
  46. Doolette AL, Smernik RJ (2015) Quantitative analysis of 31P NMR spectra of soil extracts–dealing with overlap of broad and sharp signals. Magn Reson Chem 53(9):679–685PubMedCrossRefGoogle Scholar
  47. Draz KA (2009) Cotton pests. Faculty of Agriculture, Alexandria University, DamanhourGoogle Scholar
  48. Ecobichon DJ (2001) Pesticide use in developing countries. Toxicology 160(1-3):27–33PubMedCrossRefGoogle Scholar
  49. El-Wakeil NE, Gaafar NM, Vidal S (2006) Side effect of some neem products on natural enemies of Helicoverpa (Trichogramma spp.) and Chrysoperla carnea. Arch Phytopathol Plant Protect 39(6):445–455CrossRefGoogle Scholar
  50. Eunice MA (2013) Real time paddy crop field monitoring using Zigbee Network. Int J Eng Sci Res 4(1):1208–1213Google Scholar
  51. Farid HU, Bakhsh A, Ahmad N, Ahmad A (2013) Evaluation of management zones for site-specific application of crop inputs. Pak J Life Soc Sci 11(1):29–35Google Scholar
  52. Farokhzadeh S, Alifakheri B (2014) Marker-assisted selection for disease resistance: applications in breeding (Review). Int J Agric Crop Sci 7:1392–1405Google Scholar
  53. Fernández-Quintanilla C, Peña JM, Andújar D, Dorado J, Ribeiro A, López-Granados F (2018) Is the current state of the art of weed monitoring suitable for site-specific weed management in arable crops? Weed Res 58(4):259–272CrossRefGoogle Scholar
  54. Finch-Savage WE, Bassel GW (2016) Seed vigour and crop establishment: extending performance beyond adaptation. J Exp Bot 67(3):567–591PubMedCrossRefGoogle Scholar
  55. Foglia MM, Reina G (2006) Agricultural robot for radicchio harvesting. J Field Robot 23(6-7):363–377CrossRefGoogle Scholar
  56. Garcia-Vila M, Fereres E, Mateos L, Orgaz F, Steduto P (2009) Deficit irrigation optimization of cotton with AquaCrop. Agron J 101(3):477–487CrossRefGoogle Scholar
  57. Gencsoylu I, Yalcin I (2004) The effect of different tillage systems on cotton pests and predators in cotton fields. Asian J Plant Sci 3(1):39–44CrossRefGoogle Scholar
  58. Gitonga GW (1995) Forecasting maize crop yield in Kenya using meteorological satellite data. In: Proceedings of the EUMETSAT Meteorological Satellite Data Users’ Conference: Polar Orbiting Systems, Winchester, UK, 1995, pp 93–100Google Scholar
  59. Glowienka E, Michalowska K, Pekala A, Hejmanowska B (2016) Application of GIS and remote sensing techniques in multitemporal analyses of soil properties in the foreland of the carpathians. IOP Conf Ser Earth Environ Sci 44(5):052044CrossRefGoogle Scholar
  60. Goglio P, Brankatschk G, Knudsen MT, Williams AG, Nemecek T (2018) Addressing crop interactions within cropping systems in LCA. Int J Life Cycle Assess 23(9):1735–1743CrossRefGoogle Scholar
  61. Gonzalez-de-Soto M, Emmi L, Benavides C, Garcia I, Gonzalez-de-Santos P (2016) Reducing air pollution with hybrid-powered robotic tractors for precision agriculture. Biosyst Eng 143:79–94CrossRefGoogle Scholar
  62. Grunwald S, Vasques GM, Rivero RG (2015) Fusion of soil and remote sensing data to model soil properties. Adv Agron 131:1–109CrossRefGoogle Scholar
  63. Guan S, Fukami K, Matsunaka H, Okami M, Tanaka R, Nakano H, Sakai T, Nakano K, Ohdan H, Takahashi K (2019) Assessing correlation of high-resolution NDVI with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sens (Basel) 11(2):112CrossRefGoogle Scholar
  64. Haibo L, Qing L, Yufeng X, Chuijje Y (2010) Research and development on the key technology of wheat single seed robot. In: IEEE World Automation Congress. IEEE, Washington, DC, pp 339–343Google Scholar
  65. Hassan-Esfahani L, Torres-Rua A, Jensen A, McKee M (2015) Assessment of surface soil moisture using high-resolution multi-spectral imagery and artificial neural networks. Remote Sens (Basel) 7(3):2627–2646CrossRefGoogle Scholar
  66. Hebbar KB, Venugopalan MV, Seshasai MVR, Rao KV, Patil BC, Prakash AH, Kumar V, Hebbar KR, Jeyakumar P, Bandhopadhyay KK, Rao MRK, Khadi BM, Aggarwal PK (2008) Predicting cotton production using Info crop-cotton simulation model, remote sensing and spatial agro-climatic data. Curr Sci 95:1570–1579Google Scholar
  67. Hedley C (2015) The role of precision agriculture for improved nutrient management on farms. J Sci Food Agric 95(1):12–19PubMedCrossRefGoogle Scholar
  68. Held A, Hudson J, Martin L, Reeves W (2016) Benefits and safety of glyphosate. MONSANTO, St. Louis, MOGoogle Scholar
  69. Henneberry TJ (2007) Integrated systems for control of the pink bollworm Pectinophora gossypiella in cotton. In: Vreysen MJB, Robinson AS, Hendrichs J (eds) Area-wide control of insect pests. Springer, Dordrecht, pp 567–579CrossRefGoogle Scholar
  70. Holtzapffel R, Mewett O, Wesley V, Hattersley P (2008) Genetically modified crops: tools for insect pest and weed control in cotton and canola. Australian Government Bureau of Rural Sciences, Canberra, ACTGoogle Scholar
  71. Hoogenboom G, Jones JW, Wilkens PW, Porter CH, Batchelor WD, Hunt LA, Boote KJ, Singh U, Uryasev O, Bowen WT, Gijsman AJ, Du Toit AS, White JW, Tsuji GY (2004) Decision support simulating cotton yield using CROPGRO model system for agrotechnology transfer. ver. 4.0. University of Hawaii, Honolulu, HawaiiGoogle Scholar
  72. Hoogenboom G, Porter CH, Shelia V, Boote KJ, Singh U, White JW, Hunt LA, Ogoshi R, Lizaso JI, Koo J, Asseng S, Singels A, Moreno LP, Jones JW (2017) Decision Support System for Agrotechnology Transfer (DSSAT), Version 4.7. DSSAT Foundation, Gainesville, FL. Available at: Google Scholar
  73. Hunt ER, Horneck DA, Spinelli CB, Turner RW, Bruce AE, Gadler DJ, Brungardt JJ, Hamm PB (2018) Monitoring nitrogen status of potatoes using small unmanned aerial vehicles. Precis Agric 19(2):314–333CrossRefGoogle Scholar
  74. Incrocci L, Marzialetti P, Incrocci G, Di A, Balendonck J, Bibbiani C, Bibbiani C, Spagnol S, Pardossi A (2019) Sensor-based management of container nursery crops irrigated with fresh or saline water. Agric Water Manag 213:49–61CrossRefGoogle Scholar
  75. James C (2016) Global status of commercialized biotech/GM crops: 2016: ISAAA brief No. 52. ISAAA, Ithaca, NYGoogle Scholar
  76. Jensen JR (2009) Remote sensing of the environment: an earth resource perspective, 2nd edn. Pearson Education India, NoidaGoogle Scholar
  77. Jia B, He H, Ma F, Diao M, Jiang G, Zheng Z, Cui J, Fan H (2014) Use of a digital camera to monitor the growth and nitrogen status of cotton. Scientific World Journal 2014:602647PubMedGoogle Scholar
  78. Jiang GL (2013) Molecular markers and marker-assisted breeding in plants. Chapter 3. In: Plant breeding from laboratories to fields. InTech, Rijeka, pp 45–83Google Scholar
  79. Johnson WC, Brenneman TB, Baker SH, Johnson AW, Sumner DR, Mullinix BG (2001) Tillage and pest management considerations in a peanut–cotton rotation in the southeastern Coastal Plain. Agron J 93(3):570–576CrossRefGoogle Scholar
  80. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18(3–4):235–265CrossRefGoogle Scholar
  81. Kale S, Khandagale S, Gaikwad S, Narve S, Gangal P (2015) Agriculture drone for spraying fertilizer and pesticides. Int J Adv Res Comp Sci Softw Eng 5(12):804–807Google Scholar
  82. Kamal RM, Amin MSM (2010) GIS-based irrigation water management for precision farming of rice. Int J Agric Biol Eng 3(3):27–35Google Scholar
  83. Kamau-Rewe M, Rasche F, Cobo JG, Dercon G, Shepherd KD, Cadisch G (2011) Generic prediction of soil organic carbon in alfisols using diffuse reflectance Fourier-transform mid-infrared spectroscopy. Soil Sci Soc Am J 75:2358–2360CrossRefGoogle Scholar
  84. Kashyap N, Das KN, Deka B, Dutta M (2018) Soil test based fertilizer prescriptions under integrated plant nutrient supply for hybrid rice (cv. US–382) in alluvial soils of Jorhat District of Assam, India. Int J Curr Microbiol App Sci 7(5):3570–3576CrossRefGoogle Scholar
  85. Kerselaers E, Rogge L, Lauwers G, Van Huylenbroeck G (2015) Decision support for prioritizing of land to be presented for agriculture: can participatory tool development help? Comp Electron Agric 110:208–220CrossRefGoogle Scholar
  86. Khalifa EM, Kholief RM, Eitawil MA, Neamatallah MA (2009) Influence of harvesting methods on ginning operation and fiber qualities for Egyptian cotton. Mansoura Uni J Agric Sci 34:1431Google Scholar
  87. Khan MB, Khaliq A, Ahmad S (2004) Performance of mashbean intercropped in cotton planted in different planting patterns. J Res (Sci) 15(2):191–197Google Scholar
  88. Khanal S, Fulton J, Shearer S (2017) An overview of current and potential applications of thermal remote sensing in precision agriculture. Comp Electron Agric 139:22–32CrossRefGoogle Scholar
  89. Krištof K, Šima T, Nozdrovický L, Findura P (2014) The effect of soil tillage intensity on carbon dioxide emissions released from soil into the atmosphere. Agron Res 12(1):115–120Google Scholar
  90. Kumar J, Choudhary AK, Solanki RK, Pratap A (2011) Towards marker-assisted selection in pulses: a review. Plant Breed 130(3):297–313CrossRefGoogle Scholar
  91. Kumar A, Brar NS, Pal S, Singh P (2017a) Available soil macro and micro-nutrients under rice-wheat cropping system in District Tarn Taran of Punjab, India. Ecol Environ Conserv 23(1):201–206Google Scholar
  92. Kumar S, Niwas R, Khichar ML, Singh A, Badal P, Kumar Y, Chauthan AS (2017b) Genetic coefficient and validation of DSSAT model for cotton under different growing environments. Int J Curr Microbiol App Sci 6(4):1031–1041CrossRefGoogle Scholar
  93. Kumar U, Shahid M, Tripathi R, Mohanty S, Kumar A, Bhattacharyya P, Lal B, Gautam P, Raja R, Panda BB, Jambhulkar NN, Shukla AK, Nayak AK (2017c) Variation of functional diversity of soil microbial community in sub-humid tropical rice-rice cropping system under long-term organic and inorganic fertilization. Ecol Indic 73:536–543CrossRefGoogle Scholar
  94. Lal S (2016) Impact of plant diversity on the insect pest complex of maize. Doctoral Dissertation, MPUAT, UdaipurGoogle Scholar
  95. Latif A, Rao AQ, Khan MAU, Shahid N, Bajwa KS, Ashraf MA, Abbas MA, Azam M, Shahid AA, Nasir IA, Husnain T (2015) Herbicide-resistant cotton (Gossypium hirsutum) plants: an alternative way of manual weed removal. BMC Res Notes 8(1):453PubMedPubMedCentralCrossRefGoogle Scholar
  96. Lema M (2018) Marker assisted selection in comparison to conventional plant breeding: review article. Agric Res Tech Open Access J 14(2):555914. CrossRefGoogle Scholar
  97. Lepage M, Simonneaux V, Thomas S, Metral J, Duchemin B, Kharrou H, Cherkaoui M, Chehbouni A (2009) SAMIR a tool for irrigation monitoring using remote sensing for evapotranspiration estimate. MELIA, MarrakechGoogle Scholar
  98. Li W, Zhou Z, Meng Y, Xu N, Fok M (2009) Modeling boll maturation period, seed growth, protein, and oil content of cotton (Gossypium hirsutum L.) in China. Field Crop Res 112:131–140CrossRefGoogle Scholar
  99. Lim SL, Wu TY, Lim PN, Shak KP (2015) The use of vermicompost in organic farming: overview, effects on soil and economics. J Sci Food Agric 95(6):1143–1156PubMedCrossRefGoogle Scholar
  100. Lindblom J, Lundstrom C, Ljung M, Jonsson A (2017) Promoting sustainable intensification in precision agriculture: review of decision support system development and strategies. Precis Agric 18:309–331CrossRefGoogle Scholar
  101. Liu J, Zehnder AJB, Yang H (2009) Global consumptive water use for crop production: the importance of green water and virtual water. Water Resour Res 45(5):1–15CrossRefGoogle Scholar
  102. López-Granados F (2011) Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res 51(1):1–11CrossRefGoogle Scholar
  103. Ludibeth SM, Marina IE, Vicenta EM (2012) Vermicomposting of sewage sludge: earthworm population and agronomic advantages. Compost Sci Util 20(1):11–17CrossRefGoogle Scholar
  104. Ma Y, Liu S, Song L, Xu Z, Liu Y, Xu T, Zhu Z (2018) Estimation of daily evapotranspiration and irrigation water efficiency at a Landsat-like scale for an arid irrigation area using multi-source remote sensing data. Remote Sens Environ 216:715–734CrossRefGoogle Scholar
  105. Malik M, Sehgal M, Kanojia AK, Singh RV (2018) A review paper on decision support system/expert system developed on mango. Int J Plant Protec 11(1):119–123CrossRefGoogle Scholar
  106. Mangalassery S, Sjögersten S, Sparkes DL, Sturrock CJ, Craigon J, Mooney SJ (2014) To what extent can zero tillage lead to a reduction in greenhouse gas emissions from temperate soils? Sci Rep 4:4586PubMedPubMedCentralCrossRefGoogle Scholar
  107. Maraseni TN, Cockfield G (2011) Does the adoption of zero tillage reduce greenhouse gas emissions? An assessment for the grains industry in Australia. Agr Syst 104(6):451–458CrossRefGoogle Scholar
  108. Marino S, Alvino A (2018) Detection of homogeneous wheat areas using multi-temporal UAS images and ground truth data analyzed by cluster analysis. Eur J Remote Sens 51(1):266–275CrossRefGoogle Scholar
  109. Masseroni D, Moller P, Tyrell R, Romani M, Lasagna A, Sali G, Facchi A, Gandolfi C (2018) Evaluating performances of the first automatic system for paddy irrigation in Europe. Agric Water Manag 201:58–69CrossRefGoogle Scholar
  110. McBratney A, Whelan B, Ancev T, Bouma J (2005) Future directions of precision agriculture. Precis Agric 6:7–23CrossRefGoogle Scholar
  111. de Medeiros FHV, de Souza RM, Ferro HM, Zanotto E, Machado JDC, de Medeiros FCL (2015) Screening of endospore-forming bacteria for cotton seed treatment against bacterial blight and damping-off. Adv Plants Agric Res 2(4):167–172Google Scholar
  112. Minasny B, Tranter G, McBratney AB, Brough DM, Murphy BW (2009) Regional transferability of mid-infrared diffuse reflectance spectroscopic prediction for soil chemical properties. Geoderma 153(1–2):155–162CrossRefGoogle Scholar
  113. Mir SA, Qasim M, Arfat Y, Mubarak T, Bhat ZA, Bhat JA, Bangroo SA, Sofi TA (2015) Decision support systems in a global agricultural perspective - a comprehensive review. Int J Agric Sci 7(1):403–415Google Scholar
  114. MIT (2016) Six ways drones are revolutionizing agriculture. MIT Technol RevGoogle Scholar
  115. Mogili UMR, Deepak BBVL (2018) Review on application of drone systems in precision agriculture. Proc Comp Sci 133:502–509CrossRefGoogle Scholar
  116. Montesano FF, Serio F, Mininni C, Signore A, Parente A, Santamaria P (2015) Tensiometer-based irrigation management of subirrigated soilless tomato: effects of substrate matric potential control on crop performance. Front Plant Sci 6:1150PubMedPubMedCentralCrossRefGoogle Scholar
  117. Murthy VRK (2004) Crop growth modeling and its applications in agricultural meteorology. In: Sivakumar MVK, Roy PS, Harsen K, Saha SK (eds) Satellite Remote Sensing and GIS Applications in Agricultural Meteorology Workshop, Dehra Dun, India. World Meteorological Organisation, Geneva, pp 235–261Google Scholar
  118. Muthamilselvan M, Rangasamyt K, Ananthakrishnan D, Manian R (2007) Mechanical picking of cotton - a review. Agric Rev 28(2):118–126Google Scholar
  119. Najar IA, Khan AB (2013) Management of fresh water weeds (macrophytes) by vermicomposting using Eisenia fetida. Environ Sci Pollut Res 20(9):6406–6417CrossRefGoogle Scholar
  120. Naranjo SE (2009) Impacts of Bt crops on non-target invertebrates and insecticide use patterns. CAB Rev Perspect Agric Veterin Sci Nutr Nat Resour 4(011):1Google Scholar
  121. Nasrullah HM, Aslam M, Akhtar M, Ali B, Majid A, Akram M, Farooq U (2017) Relay cropping of cotton in standing wheat: an innovative approach for enhancing the productivity and income of small farm. Roman Agric Res 34:187–195Google Scholar
  122. North JH, Gore J, Catchot AL, Stewart SD, Lorenz GM, Musser FR, Cook DR, Kerns DL, Dodds DM (2017) Value of neonicotinoid insecticide seed treatments in mid-south cotton (Gossypium hirsutum [Malvales: Malvaceae]) production systems. J Econ Entomol 111(1):10–15CrossRefGoogle Scholar
  123. Ogunti EO, Akingbade FK, Segun A, Oladimeji O (2018) Decision support system using mobile applications in the provision of day to day information about farm status to improve crop yield. Periodic Eng Nat Sci 6:89–99Google Scholar
  124. Ortiz BV, Hoogenboom G, Vellidis G, Boote KJ, Davis RF, Perry C (2009) Adapting the CROPGRO cotton model to simulate cotton biomass and yield under southern root-knot nematode parasitism. Trans ASABE 52:2129–2140CrossRefGoogle Scholar
  125. Ota T, Bontsema J, Hayashi S, Kubota K, van Henten E, van Os EA, Ajiki K (2007) Development of a cucumber leaf picking device for greenhouse production. Biosyst Eng 98:381–390CrossRefGoogle Scholar
  126. Padilla FM, Gallardo M, Peña-Fleitas MT, de Souza R, Thompson RB (2018) Proximal optical sensors for nitrogen management of vegetable crops: a review. Sensors (Basel) 18(7):E2083CrossRefGoogle Scholar
  127. Panwar R (2015) GIS and remote sensing applications in natural resources management. Int J Innov Res Adv Stud 2(4)Google Scholar
  128. Papadopoulos AV, Kati V, Chachalis D, Kotoulas V, Stamatiadis S (2018) Weed mapping in cotton using ground-based sensors and GIS. Environ Monit Assess 190(10):622PubMedCrossRefGoogle Scholar
  129. Papageorgiou EI, Markinos AT, Gemtos TA (2011) Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Appl Soft Comput 11(4):3643–3657CrossRefGoogle Scholar
  130. Pathak TB, Jones JW, Fraisse C, Wright D, Hoogenboom G, Judge J (2009) Uncertainty analysis of CROPGRO-cotton model. In: American Geophysical Union, Fall Meeting. AGU, Washington, DCGoogle Scholar
  131. Pathan SA, Hate SG (2016) Automated Irrigation system using Wireless Sensor Network. Int J Eng Tech Res 5(6):6–9CrossRefGoogle Scholar
  132. Patil SS, Patil VC, Al-Gaadi KA (2011) Spatial variability in fertility status of surface soils. World Appl Sci J 14:1020–1024Google Scholar
  133. Pawar J, Khanna R (2018) More crop per drop: ways to increase water use efficiency for crop production: a review. Int J Chem Stud 6(3):3573–3578Google Scholar
  134. Pedigo LP (1989) Entomology and pest management. McMillan Publishing Company, New York, NY, p 646Google Scholar
  135. Pilli SK, Nallathambi B, George SJ, Diwanji V (2015) Eagrobot-a robot for early crop disease detection using image processing. In: 2nd International Conference on Electronics and Communication Systems. IEEE, Washington, DC, pp 1684–1689Google Scholar
  136. Pobkrut T, Kerdcharoen T (2014) Soil sensing survey robots based on electronic nose. In: 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014). IEEE, Washington, DC, pp 1604–1609CrossRefGoogle Scholar
  137. Pramanik P, Chung YR (2010) Efficacy of vermicomposting for recycling organic portion of hospital wastes using Eisenia fetida: standardization of cow manure proportion to increase enzymatic activities and fungal biomass. Environmentalist 30(3):267–272CrossRefGoogle Scholar
  138. Privette CV, Khalilian A, Torres O, Katzberg S (2011) Remote sensing of environment utilizing space-based GPS technology to determine hydrological properties of soils. Remote Sens Environ 115(12):3582–3586CrossRefGoogle Scholar
  139. Quebrajo L, Perez-Ruiz M, Pérez-Urrestarazu L, Martínez G, Egea G (2018) Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosyst Eng 165:77–87CrossRefGoogle Scholar
  140. Radhakrishnan S (2017) Sustainable cotton production. In: Sustainable fibres and textiles. Woodhead Publishing, Cambridge, pp 21–67CrossRefGoogle Scholar
  141. Rahman MH, Ahmad A, Wajid A, Hussain M, Akhtar J, Hoogenboom G (2016) Estimation of temporal variation resilience in cotton varieties using statistical models. Pak J Agric Sci 53:787Google Scholar
  142. Rahman MHU, Ahmad A, Wajid A, Hussain M, Rasul F, Ishaque W, Islam MA, Shelia V, Awais M, Ullah A, Wahid A, Sultana SR, Saud S, Khan S, Fahad S, Hussain M, Hussain S, Nasim W (2017) Application of CSM-CROPGRO-cotton model for cultivars and optimum planting dates: evaluation in changing semi-arid climate. Field Crop Res 238: 139–152. CrossRefGoogle Scholar
  143. Rahman MH, Ahmad A, Wang X, Wajid A, Nasim W, Hussain M, Ahmad B, Ahmad I, Ali Z, Ishaque W, Awais M, Muddasir M, Shelia V, Ahmad S, Fahad S, Alam M, Ullah H, Hoogenboom G (2018) Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agric For Meteorol 253-254:94–113CrossRefGoogle Scholar
  144. Rao RN, Sridhar B (2018) IoT based smart crop-field monitoring and automation irrigation system. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE, Washington, DC, pp 478–483CrossRefGoogle Scholar
  145. Rea JH, Wratten SD, Sedcole R, Cameron PJ, Davis SI, Chapman RB (2002) Trap cropping to manage green vegetable bug Nezara viridula (L.) (Heteroptera: Pentatomidae) in sweet corn in New Zealand. Agric For Entomol 4:101–107CrossRefGoogle Scholar
  146. Reddy DD, Blaise D, Kumrawat B, Singh AK (2017) Evaluation of integrated nutrient management interventions for cotton (Gossypium hirsutum) on a Vertisol in central India. Commun Soil Sci Plan 48(4):469–475Google Scholar
  147. Rekha GS, Kaleena PK, Elumalai D, Srikumaran MP, Maheswari VN (2018) Effects of vermicompost and plant growth enhancers on the exo-morphological features of Capsicum annum (Linn.) Hepper. Int J Recycl Organ Waste Agric 7(1):83–88CrossRefGoogle Scholar
  148. Ribaut JM, de Vicente MC, Delannay X (2010) Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13(2):213–218PubMedCrossRefGoogle Scholar
  149. Rocha-Munive MG, Soberón M, Castañeda S, Niaves E, Scheinvar E, Eguiarte LE, Mota-Sánchez D, Rosales-Robles E, Nava-Camberos U, Martínez-Carrillo JL, Blanco CA, Bravo A, Souza V (2018) Evaluation of the impact of genetically modified cotton after 20 years of cultivation in Mexico. Front Bioeng Biotechnol 6:82PubMedPubMedCentralCrossRefGoogle Scholar
  150. Rose DC, Morris C, Lobley M, Winter M, Sutherland WJ, Dicks LV (2018) Exploring the spatialities of technological and user re-scripting: the case of decision support tools in UK agriculture. Geoforum 89:11–18CrossRefGoogle Scholar
  151. Roth G, Harris G, Gillies M, Montgomery J, Wigginton D (2014) Water-use efficiency and productivity trends in Australian irrigated cotton: a review. Crop Pasture Sci 64(12):1033–1048CrossRefGoogle Scholar
  152. Sajjad A, Anjum SA, Ahmad R, Waraich EA (2018) Relay cropping of wheat (Triticum aestivum L.) in cotton (Gossypium hirsutum L.) improves the profitability of cotton-wheat cropping system in Punjab, Pakistan. Environ Sci Pollut Res 25(1):782–789CrossRefGoogle Scholar
  153. Sangeetha KD, Ashtaputre SA, Ramya TS, Kavyashree MC, Anil GH (2018) Exploration of fungicides against Alternaria leaf blight of cotton in Northern parts of Karnataka, India. Int J Chem Stud 6(3):2127–2129Google Scholar
  154. Sarmah K, Deka CR, Sharma U, Sarma R (2018) Role of GIS based technologies in sustainable agriculture resource planning & management using spatial decision support approach. Int J Innov Res Eng Manag 5(1):30–34CrossRefGoogle Scholar
  155. Schader C, Zaller JG, Kopke U (2005) Cotton-basil intercropping, effects on pests, yields and economical parameters in an organic field in Fayoum, Egypt. Biol Agric Hortic 23:59–72CrossRefGoogle Scholar
  156. Schut AGT, Traore PCS, Blaes X, de By RA (2018) Assessing yield and fertilizer response in heterogeneous smallholder fields with UAVs and satellites. Field Crop Res 221:98–107CrossRefGoogle Scholar
  157. Senthurpandian VK, Jayaganesh S, Srinivas S, Palani N, Muraleedharan N (2010) Application of geographic information system to fertility management of tea soils of Anamallais. Asian J Earth Sci 3(3):136–141CrossRefGoogle Scholar
  158. Shah MA, Memon N, Baloch AA (2011) Use of sex pheromones and light traps for monitoring the population of adult moths of cotton bollworms in Hyderabad, Sindh, Pakistan. Sarhad J Agric 27(3):435–442Google Scholar
  159. Shah MA, Farooq M, Hussain M (2016) Productivity and profitability of cotton–wheat system as influenced by relay intercropping of insect resistant transgenic cotton in bed planted wheat. Eur J Agron 75:33–41CrossRefGoogle Scholar
  160. Shelton AM, Badenes-Perez FR (2006) Concepts and applications of trap cropping in pest management. Annu Rev Entomol 51:285–308PubMedPubMedCentralCrossRefGoogle Scholar
  161. Shen M, Yang X, Cox-Foster D, Cui L (2005) The role of varroa mites in infections of Kashmir bee virus (KBV) and deformed wing virus (DWV) in honey bees. Virology 342(1):141–149PubMedCrossRefGoogle Scholar
  162. Shivanna AM, Nagendrappa G (2014) Chemical analysis of soil samples to evaluate the soil fertility status of selected command areas of three tanks in Tiptur Taluk of Karnataka, India. IOSR J App Chem 7(11):1–5CrossRefGoogle Scholar
  163. Sindhi SJ, Thanki JD, Desai LJ (2018) A review on integrated nutrient management (INM) approach for maize. J Pharmacogn Phytochem 7(4):3266–3269Google Scholar
  164. Singh S (2018) Transgenic cotton-its adoption, threats and challenges ahead: a review. J Entomol Zool Stud 6(5):1989–1997Google Scholar
  165. Singh A, Vasisht AK, Kumar R, Das DK (2008) Adoption of integrated pest management practices in paddy and cotton: a case study in Haryana and Punjab. Agric Econ Res Rev 21(2):1–6Google Scholar
  166. Slaughter DC, Giles DK, Downey D (2008) Autonomous robotic weed control systems: a review. Comp Electron Agric 61:63–78CrossRefGoogle Scholar
  167. Sogaard HT, Lund I (2007) Application accuracy of a machine vision-controlled robotic micro-dosing system. Biosyst Eng 96:315–322CrossRefGoogle Scholar
  168. Soil Science Society of America (2013) 2013 Annual Meeting. Available at:
  169. Srinivasan A (2006) Handbook of precision agriculture: principles and applications. Food Products Press, Binghamton, NY, pp 3–18Google Scholar
  170. Starr JL, Carneiro RG, Ruano O (2005) Nematode parasites of cotton and other tropical fibre crops. In: Luc M, Sikora RA, Bridge J (eds) Plant parasitic nematodes in sub-tropical and tropical agriculture, 2nd edn. CAB International, Wallingford, pp 539–556Google Scholar
  171. Sui R, Thomasson JA (2006) Ground-based sensing system for cotton nitrogen status determination. Trans ASABE 49(6):1983–1991CrossRefGoogle Scholar
  172. Taghvaeian S, Neale CMU, Osterberg JC, Sritharan S, Watts DR (2018) Remote sensing and GIS techniques for assessing irrigation performance: case study in Southern California. J Irrig Drain Eng 144(6):05018002CrossRefGoogle Scholar
  173. Tahir M, Ali A, Nadeem MA, Hussain A, Khalid F (2009) Effect of different sowing dates on growth and yield of wheat (Triticum aestivum L.) varieties in district Jhang, Pakistan. Pak J Life Soc Sci 7:66–69Google Scholar
  174. Tariq M, Yasmeen A, Ahmad S, Hussain N, Afzal MN, Hasanuzzaman M (2017) Shedding of fruiting structures in cotton: factors, compensation and prevention. Trop Subtrop Agroecosyst 20(2):251–262Google Scholar
  175. Tariq M, Afzal MN, Muhammad D, Ahmad S, Shahzad AN, Kiran A, Wakeel A (2018) Relationship of tissue potassium content with yield and fiber quality components of Bt cotton as influenced by potassium application methods. Field Crop Res 229:37–43CrossRefGoogle Scholar
  176. Terán-Vargas AP, Rodríguez JC, Blanco CA, Martínez-Carrillo JL, Cibrián-Tovar J, Sánchez-Arroyo H, Rodríguez-del-Bosque LA, Stanley D (2005) Bollgard cotton and resistance of tobacco budworm (Lepidoptera: Noctuidae) to conventional insecticides in southern Tamaulipas, Mexico. J Econ Entomol 98:2203–2209PubMedCrossRefGoogle Scholar
  177. Tewari S, Leskey TC, Nielsen AL, Pinero JC, Rodriguez-Saona CR (2014) Use of pheromones in insect pest management, with special attention to weevil pheromones. In: Integrated pest management. Academic Press, London, pp 141–168CrossRefGoogle Scholar
  178. Tian J, Zhang X, Yang Y, Yang C, Xu S, Zuo W, Zhang W, Dong H, Jiu X, Yu Y, Zhao Z (2017) How to reduce cotton fiber damage in the Xinjiang China. Ind Crop Prod 109:803–811CrossRefGoogle Scholar
  179. Tillman PG (2006) Sorghum as a trap crop for Nezara viridula L. (Heteroptera: Pentatomidae) in cotton in the southern United States. Environ Entomol 35(3):771–783CrossRefGoogle Scholar
  180. Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S (2002) Agricultural sustainability and intensive production practices. Nature 418:671–677PubMedCrossRefGoogle Scholar
  181. Todd SW, Hoffer RM, Milchunas DG (1998) Biomass estimation on grazed and ungrazed rangelands using spectral indices. Int J Remote Sens 19(3):427–438CrossRefGoogle Scholar
  182. Tomson M, Sahayaraj K, Kumar V, Avery PB, McKenzie CL, Osborne LS (2017) Mass rearing and augmentative biological control evaluation of Rhynocoris fuscipes (Hemiptera: Reduviidae) against multiple pests of cotton. Pest Manag Sci 73(8):743–1752CrossRefGoogle Scholar
  183. Tong YJ, Wu KM, Lu YH, Gao XW (2010) Pathogenicity of Beauveria spp. Strains to three species of mirids, Apolygus lucorum, Adelphocoris suturalis and Adelphocoris lineolatus. Acta Phytophylacica Sinca 37:172–176Google Scholar
  184. Towett EK, Shepherd KD, Cadisch G (2013) Quantification of total element concentrations in soils using total X-ray fluorescence spectroscopy (TXRF). Sci Total Environ 463-464:374–388PubMedCrossRefGoogle Scholar
  185. Tsedaley B (2015) Review on seed health tests and detection methods of seedborne diseases. J Biol Agric Healthcare 5(5):176–185Google Scholar
  186. Turan J, Višacki V, Mehandžić S, Findura P, Burg P, Sedlar A (2015) Sowing quality indicators for a seed drill with overpressure. Acta Univ Agric Silvic Mendel Brun 62(6):1487–1492CrossRefGoogle Scholar
  187. Usman M, Ahmad A, Ahmad S, Irshad M, Khaliq T, Wajid A, Hussain K, Nasim W, Chattha TM, Trethowan R, Hoogenboom G (2009) Development and application of crop water stress index for scheduling irrigation in cotton (Gossypium hirsutum L.) under semiarid environment. J Food Agric Environ 7(3–4):386–391Google Scholar
  188. Vani V, Mandla VR (2017) Comparative study of NDVI and SAVI vegetation Indices in Anantapur district semi-arid areas. Int J Civil Eng Technol 8(4):559–566Google Scholar
  189. Vijaya MBN, Rai PK, Srivastava DK, Bara BM, Kumar R (2017) Effects of polymer seed coating, fungicide seed treatment and storage duration on seedling characteristics of cotton (Gossypium hirsutum) seeds. J Pharma Phytochem 6(4):534–536Google Scholar
  190. Vleeshouwer J, Car NJ, Hornbuckle J (2015) A cotton irrigator’s decision support system and benchmarking tool using national, regional and local data. In: Int. Symposium on Environ. Software Systems. Springer, Cham, pp 187–195Google Scholar
  191. Vora VD, Rakholiya KD, Rupapara KV, Sutaria GS, Akbari KN (2015) Effect of integrated nutrient management on Bt cotton and post-harvest soil fertility under dry farming agriculture. Asian J Agric Res 9(6):350–356Google Scholar
  192. Wadodkar MR, Ravishankar T, Joshi AK (2014) Application of remote sensing techniques for soil fertility assessment. Available at:
  193. Wajid A, Ahmad A, Hussain M, Rahman MH, Khaliq T, Mubeen M, Rasul F, Bashir U, Awais M, Iqbal J, Sultana SR (2014) Modeling growth, development and seed-cotton yield for varying nitrogen increments and planting dates using DSSAT. Pak J Agric Sci 51:641–650Google Scholar
  194. Wang M, Wei J, Yuan J, Xu K (2008) A research for intelligent cotton picking robot based on machine vision. In: International Conference on Information and Automation. Zhangjiajie, China. IEEE, Washington, DCGoogle Scholar
  195. Wang S, Li X, Lu J, Hong J, Chen G, Xue X, Li J, Wei Y, Zou J, Liu G (2013) Effects of controlled-release urea application on the growth, yield and nitrogen recovery efficiency of cotton. Agric Sci 4(12):33–38Google Scholar
  196. Wang L, Hu G, Yue Y, Ye X, Li M, Zhao J, Wan J (2016) GIS-based risk assessment of hail disasters affecting cotton and its spatiotemporal evolution in China. Sustainability 8(3):1–20CrossRefGoogle Scholar
  197. Wu W, De Pauw E (2011) A simple algorithm to identify irrigated croplands by remote sensing. In: Proceedings of the 34th International Symposium on Remote Sensing of Environment (ISRSE), Sydney, Australia. Arinex, Sydney, NSW, pp 10–15Google Scholar
  198. Wu K, Lu Y, Wang Z (2009) Advance in integrated pest management of crops in China. Chin Bull Entomol 46(6):831–836Google Scholar
  199. Xia J, Cui J, Ma L, Dong S, Cui X (1998) The role of transgenic BT cotton in integrated insect pest management. Acta Gossypii Sin 11:57–64Google Scholar
  200. Xia C, Wang L, Chung BK, Lee JM (2015) In situ 3D segmentation of individual plant leaves using a RGB-D camera for agricultural automation. Sensors (Basel) 15(8):20463–20479CrossRefGoogle Scholar
  201. Yuan Z, Shen Y (2013) Estimation of agricultural water consumption from meteorological and yield data: a case study of Hebei, North China. PLoS One 8(3):e58685PubMedPubMedCentralCrossRefGoogle Scholar
  202. Zang X (1998) On the estimation of biomass of submerged vegetation using Landsat thematic mapper (TM) imagery: a case study of the Honghu Lake, PR China. Int J Remote Sens 19(l):11–20CrossRefGoogle Scholar
  203. Zhang N, Wang M, Wang N (2002) Precision agriculture – a worldwide overview. Comp Electron Agric 36(2-3):113–132CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Abdul Ghaffar
    • 1
  • Muhammad Habib ur Rahman
    • 1
    • 2
    Email author
  • Hafiz Rizwan Ali
    • 1
  • Ghulam Haider
    • 1
  • Saeed Ahmad
    • 1
  • Shah Fahad
    • 3
    • 4
  • Shakeel Ahmad
    • 5
  1. 1.Department of AgronomyMuhammad Nawaz Shareef University of AgricultureMultanPakistan
  2. 2.Institute of Crop Science and Resource Conservation (INRES) Crop Science GroupUniversity BonnBonnGermany
  3. 3.Department of AgricultureUniversity of SwabiSwabiPakistan
  4. 4.College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanP.R. China
  5. 5.Department of Agronomy, Faculty of Agricultural Sciences and TechnologyBahauddin Zakariya UniversityMultanPakistan

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