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Site‐Specific Nutrient Management for Cassava in Southern India
Author(s) -
Byju G.,
Nedunchezhiyan M.,
Hridya A. C.,
Soman Sabitha
Publication year - 2016
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2015.0263
Subject(s) - fertilizer , nutrient management , manihot esculenta , nutrient , agronomy , crop , mathematics , productivity , yield (engineering) , soil fertility , crop yield , environmental science , biology , soil water , soil science , ecology , materials science , metallurgy , economics , macroeconomics
Cassava ( Manihot esculenta Crantz.) yield in the major growing environments of India has been stagnating despite the development of high yielding varieties and increasing use of chemical fertilizers. On farm experiments were conducted to evaluate the performance of site‐specific nutrient management (SSNM). Field and crop specific NPK rates were calculated using quantitative evaluation of fertility of tropical soils (QUEFTS) model. The average 2‐yr yield advantage of SSNM over farmer fertilizer practice (FFP) was 7 Mg ha −1 . The N agronomic efficiency increase of SSNM over FFP was 32 kg kg −1 , the N recovery efficiency of SSNM was 0.14 kg kg −1 greater than that of FFP and the N physiological efficiency of SSNM was 54 kg kg −1 greater than that of FFP, whereas the partial factor productivity of SSNM was 148 kg less than that of FFP. Use of SSNM led to a reduction of fertilizer costs by an average of US$10 ha −1 crop −1 and an increase in gross return above fertilizer costs by $254 ha −1 crop −1 compared with FFP. Zone NPK recommendation maps and customized fertilizer blends were also developed. The results showed the potential of SSNM in significantly increasing yield and nutrient use efficiency of cassava. Future research is needed to validate the customized fertilizer blends and fine tune zone NPK recommendation maps which will help reduce the need for field specific modeling and intensive crop monitoring.