Wheat Yield Gap Analysis: Productivity Enhancement Practices and Factor Level Categorizations
DOI:
https://doi.org/10.53560/PPASB(62-1)1097Keywords:
Food Concerns, Wheat Yield Optimization, Yield Gap, Factors, Levels, InteractionsAbstract
The demand for wheat is rising rapidly to sustain the growing population. In Pakistan, many farmers lack awareness of the optimal utilization of input factors. This study aims to determine the optimal use of these factors to enhance wheat productivity, address food security challenges and support effective policy decisions. The new concept for categorizations of agronomic factors is identified in current study as a high, medium and low loss factors to make accurate policy decisions for food sustainability. Statistical analysis is applied to 26430 crop cut experiments. The absolute and relative yield gap analysis is applied. Category-1 (major loss factors) refer to those factors, whose probability share (%) at optimum level lies in (1-25%) and these are urea (125-175) kg, DAP (100-125) kg, other fertilizers (25) kg and spray (2-3) for pest attack. The rest of the farmers are experiencing a decline in productivity. Category-II (Medium loss factors) refers those with a probability share of (26-50%) at optimum levels and these are 4 irrigations, harvesting between April 1st and 20th and certified seed (26.06%). Category-III (Minor loss factors) includes factors with a probability share of 51% or above at optimum levels such as November planting and soil type. A rise in the probability ratio of area share for categories I to III at their optimum levels results in enhancement in wheat productivity in diminishing order.
References
M. Islam, F. Shehzad, A. Qayyum, M.W. Abbas, and R. Siddiqui. Growth analysis of production of food crops and population growth for food security in Pakistan. Proceedings of the Pakistan Academy of Sciences: B. Life and Environmental Sciences 60(1): 83-90 (2023).
F. Shehzad, M. Islam, A. Ali, A. Qayyum, and R. Siddiqui. Integrating exponential regression model optimizations for wheat area, productivity and population through statistical and machine learning approaches. Pakistan journal of Botany 55(5): 1813-1818 (2023).
FAO. How to Feed the World in 2050. Paper presented at the executive summary proceedings of the expert meeting on how to feed the world in 2050. Food and Agriculture Organization Rome, Italy (2009). https://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf
B.K. Behera, P.K. Rout, and S. Behera (Eds.). Move towards zero hunger. Springer, Singapore pp. 1-35 (2019).
M. Islam. Factors affecting major food crops production: A case study of district Bahawalpur. M.Phil. Thesis. The Islamia University of Bahawalpur, Punjab, Pakistan (2017).
M. Islam, F. Shehzad, M. Omar, A. Qayyum, and R. Siddiqui. Integrating machine learning models for linear and exponential regression to predict wheat area, productivity and population. Sarhad Journal of Agriculture 38(3): 894-901 (2022).
M. Islam and F. Shehzad. A prediction model optimization critiques through centroid clustering by reducing the sample size, integrating statistical and machine learning techniques for wheat productivity. Scientifica 2022: 7271293 (2022).
S. Abid, N. Jamal, M.Z. Anwar, and S. Zahid. Exponential growth model for forecasting of area and production of potato crop in Pakistan. Pakistan Journal of Agricultural Research 31(1): 24-28 (2018).
A. Farooq, M. Ishaq, S. Yaqoob, and K.N Sadozai. Varietal adoption effect on wheat crop production in irrigated areas of NWFP. Sarhad Journal of Agriculture 23(3): 807-814 (2007).
I.A. Khan. Vision 2030: University of Agriculture, Faisalabad (2014). https://uaf.edu.pk/downloads/vision2030.pdf
M. Ahmad and U. Farooq. The state of food security in Pakistan: Future challenges and coping strategies. The Pakistan Development Review 49(4): 903-923 (2010).
J.K. Bajkani, K. Ahmed, M. Afzal, A.R. Jamali, I.B. Bhatti, and S. Iqbal. Factors affecting wheat production in Balochistan Province of Pakistan. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS) 7(12): 73-80 (2014).
A. Hussain. Economic analysis of staple food-grain crops: varieties input-output comparison, economic practices and significance in the economy of district Swat. Ph.D. Thesis. University of Peshawar, Pakistan (2010).
A. Tariq, N. Tabasa, K. Bakhsh, M. Ashfaq, and S. Hassan. Food security in the context of climate change in Pakistan. Pakistan Journal of Commerce and Social Sciences (PJCSS) 8(2): 540-550 (2014).
M. Abbas, A.D. Sheikh, H.M. Sabir, and S. Nighat. Factors responsible for low wheat productivity in central Punjab. Pakistan Journal of Agricultural Sciences 42: 3-4 (2005).
M.A. Ali, M. Ali, M. Sattar, and L. Ali. Sowing date effect on yield of different wheat varieties. Journal of Agricultural Research 48(2): 157-162 (2010).
M.K. Van-Ittersum and K.G. Cassman. Yield gap analysis Rationale, methods and applications, Introduction to the Special Issue. Field Crops Research 143: 1-3 (2013).
J.L. Hatfield and B.L. Beres. Yield Gaps in Wheat, path to enhancing productivity. Frontiers in Plant Science 10: 1603 (2019).
M. Van-Dijk, T. Morley, R. Jongeneel, M. Van-Ittersum, P. Reidsma, and R. Ruben. Disentangling agronomic and economic yield gaps: An integrated framework and application. Agricultural Systems 154: 90-99 (2017).
M. Islam. Integrating statistical and machine learning techniques to predict wheat production in Pakistan. Ph.D. Thesis. The Islamia University of Bahawalpur, Punjab, Pakistan (2022).
A. Qayyum and H.M.J. Shera. Method of area frame sampling using probability proportional to size sampling technique for crops’ surveys: a case study in Pakistan. Journal of Experimental Agriculture International 41(2): 1-10 (2019).
A. Qayyum. A Model based wheat yield estimation in the Punjab, Pakistan. Ph.D. Thesis. GC University Lahore, Punjab, Pakistan (2011).
A. Qayyum and M.K. Pervaiz. A detailed descriptive study of all the wheat production parameters in Punjab, Pakistan. African Journal of Agricultural Research 8(31): 4209-4230 (2013).
D.N. Gujarati and D.C. Porter (Eds.). Basic econometrics. McGraw Hill Inc., New York (2009).
P. Van-Oort, K. Saito, I. Dieng, P. Grassini, K. Cassman, and M. Van-Ittersum. Can yield gap analysis be used to inform R&D prioritisation. Global Food Security 12: 109-118 (2017).
A. Hameed, M. Islam, A. Qayyum, R.Siddiqui and U. Draz. Optimizing cotton productivity: A comprehensive analysis of categorizing factor levels. Journal of Pure and Applied Agriculture 8(3): 34-41 (2023).
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