Abstract
Aims: The aim of the study was to reveal the variability in soil properties influencing pigeonpea (Cajanus cajana L.) seed yield under semi-arid rainfed condition. Methods: Soils were initially classified into series level and further these series were divided into soil-phase units. For two site years viz., 2018-19 and 2019-20, surface soil samples from each soil-phase unit were collected before sowing of pigeonpea and subsequently crop growth parameters at critical stages were recorded. Results: The principal component analysis with varimax rotation resulted in seven components for both the site years, having eigenvalues greater than one, explained more than 80% of the variability. The step wise linear regression analysis showed that the pigeonpea seed yield was linearly correlated with PC3 (p<0.01), PC4 (p<0.01) and PC7 (p<0.05) of soil properties with R2 = 0.679, during 2018-19. Whereas, during 2019-20, the seed yield was linearly correlated with PC1 (p<0.01), PC3 (p<0.01) and PC6 (p<0.05) with R2 = 0.677. In site year 1, the available P2O5, Fe, Zn, S, Cu, number of pods, surface soil moisture determined the yield. In site year 2, the available K2O, P2O5, Fe, Zn, S, clay, CEC and available water content determined the yield. All these variables together explain variability in yield.
Highlights
Despite environmental perturbations, agricultural landscapes provide diverse ecosystem services and maintain crop yields (John, 2020)
The GUTmB2g1 soil phase prevailed on gentle slopes (1-3%) could give rise to a maximum yield of 1394 and 1496 kgha-1 during kharif 2018–19 and 2019–20 respectively
Of the two site years, crop yield was greater during kharif 2019–2020, due to a greater available soil moisture than that of kharif 2018–2019
Summary
Agricultural landscapes provide diverse ecosystem services and maintain crop yields (John, 2020). Namely soil texture, type of clay, soil depth to bed rock and drainage class which result primarily from the soil-forming factors such as climate, topography, parent material, biota and time, influence the land suitability to cultivate a given crop to produce maximum yield (Moore, 2001). The relationship between soil and crop yield mostly depends on the prevailing climate and the landscape. The process of deriving spatial or statistical models to establish a relationship between soil and crop yield involves a systematic inventory of land resources, soil (physico-chemical properties) and plant variables (growth and yield parameters). Efforts have been made using multivariate statistical analysis to reclassify the soil variability into spatial management units, based on principles of similarity which are analogous to crop yield
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