Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 308

expect deficiencies in potassium to occur and indeed our experience has been exactly that in
Thailand (Attanandana and Yost, 2003). With assistance from our collaborating institution
and local research support, a study of methods for diagnosing and predicting potassium
requirements was completed. Based on this result and when integrated with other
preliminary research, a tentative potassium prediction model was proposed (Yost and
Attanandana, 2006), eq. 4.

 





 1

K requirement kg K ha  Kcritical - Ksoil / BCK x B.D. x

 

(4)

Application depth / 10 x Placement factor
Biomass removed x K content in the biomass



 

Where
K requirement = the amount of fertilizer K that is needed to restore the soil K supply such
that crop yields were maximum
Kcritical = The level of soil K needed to ensure that maximum growth and productivity
occurred
Ksoil = The measured level of soil K
BCK = The soil buffer coefficient, i.e. the reactivity of the soil to added K, using the same
extractant as Ksoil
B.D. = Soil bulk density (specific gravity), i.e. the weight of soil per unit volume
Application depth = The intended depth of incorporation of the fertilizer K in cm
Placement factor = A fraction that represents the relative benefit from application to a
fraction of the soil volume at the specified depth to be fertilized
Biomass removed = The amount of crop bioproduct that is expected to be regularly removed
from the field
K content of the biomass = The K content of the portions of the crop that will be removed
from the field
Subsequent comparisons of yield and profit from farmer practice as compared with decision-
aid recommendations indicated yield increases where K was applied according to predictions
and increases in profit (Attanandana et al., 2008). Further and more detailed studies indicated
that new methods for K diagnosis should be considered (Nilawonk et al., 2008).

6.3.13 Rock phosphate module
Another substantial gap in the nutrient management of crops for food and fuel in the
Tropics included the need to consider locally available sources of nutrient phosphorus. This
was an issue both in Thailand and in Mali, West Africa. A systematic analysis of the issues
and factors that control rock phosphate effectiveness was carried out and the results were
organized into a decision-tree and logical sequence (Sidibé et al., 2004). This author
proposed a comprehensive approach to determining whether and how much of a specified
rock phosphate material should be applied to restore crop productivity. The result was an
algorithm that successfully predicted rock phosphate applications in acid sulfate soils of
Thailand (Yampracha et al., 2005).

6.3.14 Perennial crops module
Initially, the project anthropologist observed that local communities had long preferred perennial
crops for a variety of reasons later to become apparent (Colfer, 1991; Colfer et al., 1989).