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. 2023 Aug 17:10:944.
doi: 10.12688/f1000research.53095.3. eCollection 2021.

Variability in soil properties influencing pigeonpea ( Cajanus cajana L.) yield: a multivariate statistical analysis

Rajesh N L  1 , Narayana Rao K  1 , Sathishkumar U  2 , Wali V B  3 , Basavaraj K  1 , Rudramurthy H V  1 , Desai B K  4
Affiliations

Variability in soil properties influencing pigeonpea ( Cajanus cajana L.) yield: a multivariate statistical analysis

Rajesh N L et al. F1000Res. .

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 R 2 = 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 R 2 = 0.677. In site year 1, the available P 2O 5, Fe, Zn, S, Cu, number of pods, surface soil moisture determined the yield. In site year 2, the available K 2O, P 2O 5, Fe, Zn, S, clay, CEC and available water content determined the yield. All these variables together explain variability in yield.

Keywords: Principal component regression analysis; Soil-phase unit; Soil-plant relationship.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Location map with soil physiography interpreted imagery of LISS IV merged Cartosat-1.
Figure 2.
Figure 2.. Mean monthly weather parameters of Kalmandari Tanda-1 MWS during 2018.
Figure 3.
Figure 3.. Mean monthly weather parameters of Kalmandari Tanda-1 MWS during 2019.
Figure 4.
Figure 4.. Methodological workflow.
Figure 5.
Figure 5.. Soil phases of Kalmandari Tanda -1 micro watershed.
Figure 6.
Figure 6.. Pigeon pea crop yield (kg.ha -1) during 2018–19 and 2019–20.
See this image and copyright information in PMC

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