Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

In this study we used the PhenoRice algorithm to track recent variations of rice cultivation practices along the Senegal River Valley. Time series of MODIS imagery with 250m spatial resolution and a nominal 8-days frequency
were used as input for the algorithm to map the spatial and temporal variations of rice cultivated area and of several important phenological metrics (e.g., crop establishment and harvesting dates, length of season) for the 2003–2016 period in both the dry and the wet rice cultivation seasons. Comparison between PhenoRice
results and ancillary and field data available for the Senegal part of the study area showed that the algorithm is
able to track the interannual variations of rice cultivated area, despite the total detected rice area being consistently
underestimated. PhenoRice estimates of crop establishment and harvesting dates resulted accurate
when compared with field observations available for two sub-regions for a period of 10 years, and thus allow
assessing interannual variability and tracking changes in agronomic practices. An analysis of interannual trends
of rice growing practices based on PhenoRice results highlighted a clear shift of rice cultivation from the wet to
the dry season starting approximately from 2008. The shift was found to be particularly evident in the delta part of the SRV. Additionally, a statistically significant trend was revealed starting 2006 towards a longer dry season (r2=0.81; Slope=1.24 days y−1) and a shorter wet season (r2=0.65; Slope=0.53 days y−1). These findings are in agreement with expert knowledge of changes ongoing in the area. In particular the shorter wet season is
attributed to shortage of labor and equipment leading to a delay in completion of harvesting operations in the
dry season, which led to the adoption of short-duration rice varieties by farmers in the wet season to avoid risk of
yield losses due to climatic constraints. Aforementioned results highlight the usefulness of the PhenoRice algorithm
for providing insights about recent variations in rice cultivation practices over large areas in developing
countries, where high-quality up to date information about changes in agricultural practices are often lacking.