Elaboration and test of a decision rule for the application of mepiquat chloride on cotton in Mali

Farmers often make complex management decisions during a cropping cycle. To design new cropping systems that go beyond standard setups and are better adapted to local constraints, agronomists must formalize these farmer's decisions into decision rules that can be tested and disseminated. In Mali, there has been a marked decrease in cotton productivity over the last 10 years, whereas the area planted with cotton has doubled. However, cropping recommendations remain almost the same throughout the country. Cotton researchers have been asked to put forward new technical proposals suited to a wider range of socio-economic and biophysical cropping conditions.The aim of this study was to develop a methodology to build a decision rule (DR) to help Malian farmers in making decisions on the use of growth regulators in cotton fields.In the first phase, a 2-year experiment was conducted in two experimental stations, under a wide range of cotton vegetative growth conditions, along with different stand densities and fertilization rates. Mepiquat chloride (MC) was applied to half of the plots, and its effects were then computed against selected vegetative growth indicators measured before the date of MC application. A DR was proposed on this basis. In the second phase, the DR was tested in a six-village experiment. MC was applied in plots on 15 farms in each village and its effects on yield were recorded.The results showed that is possible to build a DR for MC application based on LAI or aerial biomass indicators. The usual indicators, such as the five-node length technique, were not found to be useful. The response of cotton plots to MC for a given level of vegetative growth remained scattered, as other factors probably interfered, such as the length of the rainy season. Testing the DR in farmers’ fields showed that it was useful in determining appropriate MC applications. The usefulness of the DR is discussed on the basis of its accuracy and on the complexity of the selected indicator.