Improving Farmers' Revenue in Crop Rotation Systems with Plot Adjacency Constraints in Organic Farms with Nutrient Amendments

The search for sustainable agriculture is leading many economies to turn to crop rotation systems and to abandon monoculture systems which generally require increased use of pesticides and synthetic fertilizers. But the optimization of crop rotation remains a challenge, especially when considering organic farming. This work tackles the optimization of crop rotation in traditional organic farms with plot adjacency constraints and nutrient amendments. In the present configuration, each farmer owns a certain quantity of rudimentary equipment and a number of workers, all considered as resources. Farms are subdivided into plots and each plot allows only one crop at a given period. At a given interval of time, each plot receives a certain quantity of nutrient. The generated rotations are of fixed durations for all plots and the objective is to maximize farmers' income. A linear programming approach is used to determine the solution of the proposed farming model. Three levels of constraints are combined in the linear program to generate realistic rotations: (i) biophysical constraints including crop succession and plot adjacency; (ii) structural constraints including budget and resources; (iii) organizational constraints such as nutrient amendment and market demand. To evaluate the performance of the model, scenarios based on real-world data has been defined and solved using free solvers. The solutions obtained indicate that all the constrains are satisfied. In addition, farmers' revenue is improved, reaching a stationary position when the quantity of available resources is equal or greater than the quantity of required resources. Finally, Cbc solver is faster than GLPK solver; and it provides solutions on larger instances where GLPK does not.

Automated summary

The search for sustainable agriculture is leading economies to shift to crop rotation systems, abandoning monoculture systems. The optimization of crop rotation, especially in organic farming, remains a challenge. This study uses linear programming to optimize crop rotation in traditional organic farms, maximizing farmers' income by combining biophysical, structural, and organizational constraints for realistic rotations.