Research on the Resources Management and Risk Efficiency for Crop Rotation Systems in Sudan Gezira Scheme

Risk is an important factor in crop rotation systems and cropping system management studies. The
study uses stochastic simulation techniques and Stochastic Efficiency with Respect to Function
(SERF) to evaluate five crop rotations risk-efficiency and economic sustainability in Sudan Gezira
Scheme. Price and yield risk for five crops were simulated to calculate whole-scheme net return. The
analysis shows with the present irrigation system capacity 4 course rotation deal with potential at
upside level and is the most preferred at lower (ARAC). The 5 Course rotations (A) with 72% is most
risk efficiency followed by (B) with 53% land use intensity and mitigate risk at downside level. The 5
course rotations achieve water distribution equity and is the most risk efficient crop rotation at upper
(ARAC). The area allocated in 5 Course rotations (A) for cotton crop is 11%, wheat 34%, sorghum
41%, groundnut 11% and fodder crops 3%. Fodder can be grown two times in summer and winter
season without creating water shortage problems and add value to livestock products. However, this
will increase net return and increase soil fertilities within the selected crop rotation. The result also
shows that return pack to night storage irrigation system needs a risk premium of 36 Million (SDG)
and reduce return loss for farmers at tail end canals. The techniques used in this study could be used
with any distribution estimates for evaluation uncertain variables incorporated in crop rotation such as
new crop varieties and research recommendation packages. They also could be modified to account
for new information contribution during the decision process and account for dynamic effects and
policy adjustment and modification. In order to utilize stochastic simulation techniques the study use
data from farmer’s field, it is critical that there is enough information to characterize the distributions of
the uncertain variables. The distributions of uncertain variables in this analysis were estimated based
on historical data; however historical data distributions may not be the best representation of potential
outcomes. As a result, the techniques used in this study could be used with any distribution estimates
for the uncertain variables incorporating new crop varieties and research recommendation packages.