Optimal Land Allocation Programming Problems for Agricultural Revenue Maximization in Andhra Pradesh

DOI: 10.18805/ag.D-5019    | Article Id: D-5019 | Page : 275-279
Citation :- Optimal Land Allocation Programming Problems for Agricultural Revenue Maximization in Andhra Pradesh.Agricultural Science Digest.2020.(40):275-279
Tirupathi Rao Padi, Kiran Kumar Paidipati, Madhumita Oram kirankumarpaidipati@gmail.com
Address : Department of Statistics, Pondicherry University, Puducherry-605 014, India. 
Submitted Date : 17-08-2019
Accepted Date : 15-01-2020


This study is on development of programming problems for optimal allocation of agricultural land to different crops with special reference to Andhra Pradesh State. The prime objective of this study is to provide the management planning to farm people for optimal utilization of agricultural land resources with the objective of maximizing the revenue.  Three programming problems were formulated for the objectives of optimal crop area scheduling with (i) simple LPP, (ii) single goal programming and (iii) multiple goals programming with crop wise goals. Data from the agricultural farmers of AP were collected for ten crops cultivated with nine different methods of farming. While formulating the models, we have considered the issues of  revenue per kg, total yield of the crop, minimum support price per kg, market competitive price per kg, break even revenue, break even cost ( in thousand rupees), minimum investment,  maximum investment, available area for each crop, crop wise arbitrary target value and cost in rupees, etc. Investment for different types of expenditures like ploughing, seeds per kg, plantation, labour, fertilizers, water, rent, picking charges, storage per quintal, etc are considered. The activity ended with exploring the decision variables of finding the optimal land allocation for each crop. 


Agriculture revenue maximization Optimal crop planning Simple and multiple goal programming problems


  1. Bhawana Matsyapa, Lakhera M and Pathak H (2018). Determination of Cropping Pattern for Marginal Farmers of Dhamtari District of Chhattisgarh. Journal of Pharmacognosy and Phytochemistry. 7(3): 1289-1291.
  2. Gadge S.B. (2014). Linear Programming Approach for Allocation of Land and Water Resources in Canal Command Area under Surface Method of Irrigation- A Case Study. Inter- -national Journal of Innovative Research in Science, Engineering and Technology. 3 (Special Issue 4): 153-168.
  3. Ishtiaq Hassan, Muhammad Arif Raza, Izhar Ahmed Khan and Rehmat Ilahi (2005). Use of Linear Programming Model to Determine the Optimum Cropping Pattern, Production and Income Level: A Case Study from Dera Ghazi Khan Division. Journal of Agriculture and Social Sciences. 1(1): 32-34. 
  4. Manju S. Tonk, Hemant Poonia, Jitender Kumar Bhatia and Rekha (2019). Mathematical Programming Approach to Determine the Optimum Cropping Pattern: A Case Study. Inter- -national Journal of Agricultural Science and Research (IJASR). 9(3): 107-112.
  5. Meselu Tegenie Mellaku, Travis Reynolds and Teshale Woldeamanuel (2018). Role of Linear Programming Based Cropland Allocation to Enhance Performance of Smallholder Crop Production: A Pilot Study on Abaro Kebele, Shashemene Zuria District, West Arsi Zone, Oromiya Regional State, Ethiopia.
  6. Pushpavalli. K., Subasree P. and Umadevi S (2018). Decision Making in Agriculture: A Linear Programming Approach, International Journal of Mathematical Archive. 9(3): 120-124.
  7. Sofi N. A., Aquil Ahmed, Mudasir Ahmad and Bilal Ahmad Bhat (2015). Decision Making in Agriculture: A Linear Programming Approach. International Journal of Modern Mathematical Sciences. 13(2): 160-169.
  8. Wankhade M.O. and Lunge H. S. (2012). Allocation of Agricultural Land to the Major Crops of Saline Track by Linear Programming Approach: A Case Study. International Journal of Scientific and Technology Research. 1(9): 21-25.
  9. Zenis F M, S Supian and E Lesmana (2018). Optimization of Land Use of Agricultural Farms in Sumedang Regency by Using Linear Programming Models, IOP Conf. Series: Materials Science and Engineering. 332(1): 1-6.

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