This paper is based on an Operations Research approach and the literature on the problem of agricultural crop planning when land and water availability are limiting factors. The primary literature source is a linear-programming model created for Western Bahia, Brazil, to maximize agricultural net benefit, while accounting for crop choice, crop cycle timing, land, water, labour, machinery, capital, and hydroclimatic scenarios. To illustrate the procedure using readily available data, the official FAOSTAT data for Brazil for 2022 were downloaded for four crops: soybeans, maize, seed cotton, and dry beans. Crop yield observations and producer price observations were added, along with crop cycle and crop water coefficients reported by the primary study. Next, a planning LP was solved using the SciPy HiGHS linear-programming solver, leading to a transparent planning LP. The demonstration does not seek to calculate net profit because similar production cost data for the same crops were not available in the official data that was downloaded. Cotton is chosen only due to the maximum planning capacity of 1000 ha and illustrative water capacities. When available water is reduced by 10% and 20%, the cultivated area under the two scenarios is 571.4 hectares and 507.9 hectares, respectively, as water availability is binding. Gross revenue drops to around 12.55 million and 11.30 million and 10.04 million Brazilian local currency units, respectively. The results illustrate the applicability of linear programming to the allocation of agricultural resources and also indicate the necessity of local costs, daily weather data, labour and machinery requirements and yield-response functions before the model can be utilized as a farm decision tool.