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

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.


Introduction
Despite environmental perturbations, agricultural landscapes provide diverse ecosystem services and maintain crop yields (John, 2020).The resilience of agricultural landscapes is underpinned by complex interactions between environmental factors (precipitations, temperature, pollutants, and sunlight) and agricultural inputs provided during crop cultivation (FAO, 2013).Inherent soil properties, 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).Therefore, 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, specifically 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.
Although a soil-phase is a unit of soil outside the system of soil taxonomy, understanding the variability of landscape and soil properties and their effect on crop yield is a critical component of site-specific and sustainable management interventions (Juhos et al., 2015).Therefore, soil-phase taxa at soil series level is a functional management unit delineated based on the differentiating soil (texture, pH, EC) and land surface (gravel, slope, erosion) characteristics.Pigeonpea is a multi-purpose leguminous crop that plays an important role in food security, maintenance of soil fertility through litter fall, nitrogen fixation, provision of fodder for livestock and fuel for small-scale farmers in subsistence-agriculture (Egbe & Kalu, 2006).It improves soil organic matter content by leaf fall at the time of maturity and its deep tap root system breaks the plough pans and improves the soil structure.Hence, pigeon pea is often called as "Biological plough".Extensive ground cover by pigeonpea prevents soil erosion due to wind and water which encourages the infiltration of rainwater and smothers the weeds.
Multivariate analysis would be most appropriate to understand the specific variables influencing crop yield.Principal component analysis (PCA) is an extremely powerful analytical technique and can often indicate which variables in a data set have a significant effect on the response variables, and which are not significant (Hair et al., 2003).PCA can simplify the structure of a set of variables by replacing those with a few uncorrelated linear combinations of original variables.As soil parameters are correlated with each other due to their multicollinearity, it makes sense to examine them jointly (Zsigrai, 1999); therefore, there is the need to evaluate the correlation among the soil properties and crop growth parameters using PCAPCA is a mathematical linear transformation of the original variables, which accounts for the maximum share of the variability present in the original set of variables, with a minimum number of composite variables and makes no assumption of a model (Jolliffe & Cadima, 2016).Therefore, stepwise linear regression analysis to check the significance of soil properties and plant growth attributes is important for better understanding of the maximum share of variability present in the original set of variables.Further, these derived significant variables could be considered as primary targets for interventions to address the site-specific soil variability and improve the crop yield.

Study setting
The study area was located in the Northern dry zone of Karnataka State, India (Figure 1), between latitudes and longitudes of 17°40'17.956'' N -76°59'46.836" E and 17°38'12.689" N -77°1'17.049"E, covering 645.20 ha, at an altitude of 511 -637.86 m above mean sea level.The study was planned for two site years viz., during kharif (cropping season between June to October) 2018-2019 and kharif 2019-2020, to assess the variability in surface soil properties for each soil phase and crop growth parameters influencing pigeonpea yield.
Kalmandari Tanda-1 micro-watershed (MWS) reported mean monthly minimum and mean monthly maximum temperatures of 19.08°C and 31.97°C,respectively, during January 2018 and May 2018, whereas the reported values for these variables were respectively 18.89°C and 33.7°C for January 2019 and May 2019.The average annual rainfall measured by Kalmandari Tanda-1 MWS was 442 mm and 831.50 mm in 2018 and 2019, respectively (Figure 2 and Figure 3).

Experimental procedures.
A comprehensive methodological workflow adopted in this study is presented in Figure 4. To divide the entire microwatershed into different soil phase units, a detailed land resource inventory was initially carried out at a 1:8000 scale, using Indian Remote Sensing Satellite-P6 (IRS P6) Cartosat-1 merged LISS-IV satellite imagery (2.5 m spatial resolution) as base map to interpret the soil physiographic unit map (Figure 1).The soil (soil profile depth, number of horizons, soil color, soil structure, texture, consistency, presence of carbonates, and soil pH) and site (slope, erosion, drainage, runoff, gravelliness, stoniness, presence of rocks on surface, lithology/parent material and current land use) characteristics were recorded for all soil profile sites on a standard pro forma (Pedon description form which consists of a list of soil and site parameters that need to be recorded during a field study.A copy is provided in the Data availability section (see

Amendments from Version 2
As per the reviewer's suggestions, minor corrections have been made to the introduction, and an embedded table in Figure 6 has been removed.Extended data, Rajesh, 2021)) as per the guidelines given in Field Guide for Land Resources Inventory (Natarajan et al., 2016).

Any
In this micro-watershed, both Vertisol and Alfisol soils were found, which are soils derived from basalt and basalt-laterite intrusion parent materials, respectively (Government of India, 2008).Pedons that showed similar soil profile horizons in soil color, soil texture, soil pH, and consistence, mineral and chemical compositions were grouped into soil series (FAO, 2006) and further, these soil series were divided into 23 soil phases (Figure 5), based on surface characteristics with respect to soil texture, slope, erosion and gravelliness.
A total of 462 surface soil samples (0-30 cm) were collected in two site years.Each year, before sowing of pigeon pea, 231 soil samples were collected from the same locations (recorded using Trimble Juno 3D, a hand-held GPS) of each soil phase of the micro-watershed.Physico-chemical properties of all collected surface soil samples were analyzed using standard procedures.Erosion hazards were judged through the visible soil erosion method, by assessing the presence of rills and gullies within a field, as well as their associated deposits (Evans, 2013).Soil texture was evaluated using the feel method, and slope with the help of a dumpy level.Organic carbon (OC) was determined using the Walkley & Black (1934) wet oxidation method.Available N was determined by a modified alkaline potassium permanganate method as described by Subbiah & Asija (1956).Available P 2 O 5 was determined using Olsen's method.Available K 2 O was estimated using a flame photometer after extraction with ammonium acetate.Soil reaction (pH) was determined in 1:2.5 soil water suspensions using a glass electrode (Piper, 1966).Electrical conductivity was measured in the soil water (1:2.5)suspension using a conductivity bridge (Jackson, 1973).The level of free calcium carbonate ions in soil samples was determined using a rapid titration method with standard HCl (Piper, 1966).
Cationic micronutrients like iron, copper, manganese and zinc were extracted using diethylene triamine penta acetic acid (DTPA, 0.005 M and 0.01 M CaCl 2 + 0.1N tri-ethanol-amine at pH 7.3), and the concentration was measured using an atomic absorption spectrophotometer (ContrAA 700 Make) as outlined by Lindsay & Norvell (1978).Available boron in soil was estimated with a colorimetric method using hot water as extractant and expressed in mg kg -1 (Page et al., 1982).Available sulphur in soil was extracted with CaCl 2 .2H 2 O (0.15%), and the extract was reacted with barium chloride crystals.The intensity of the resulting turbidity was measured using a spectrophotometer at a wavelength of 420 nm (Jackson, 1973) The bulk density was estimated using Keen's cup method for the disturbed soils (Piper, 1966).Particle size distribution of soil samples was determined using the "International Pipette" method (Piper, 1966); the soil's cation exchange capacity (CEC) was determined by equilibrating the soil with 1N sodium acetate solution using a flame photometer, to calculate the CEC.The exchangeable sodium percentage (ESP) was calculated by dividing the exchangeable sodium by CEC and exchangeable bases (Ca, Mg, and K, Na), which were measured by titration and flame photometer respectively.
Pigeon pea growth and yield parameters were recorded using a crop cutting experiment, where observations were regularly made in the farmer's field at specific locations of the study area and at different crop growth stages.The growth parameters viz., plant height (in centimeter), number of branches per plant, and leaf area index (AccuPAR Ceptometer model LP-80) were recorded for each soil phase.Five pigeon pea plants were labeled in a 1 m 2 area of each soil phase unit and the growth parameters were recorded at 30, 60, and 90 days after sowing (DAS), as well as during the crop harvesting stage.Similarly, pigeon pea yield parameters, namely the number of pods per plant and grain yield (kg ha -1 ), were recorded at the harvest stage and after the harvest respectively.Details of pigeon pea varieties and fertilizer doses (urea and di-ammonium phosphate manufactured by Zuari Agro Chemical Ltd., India) used are given in the Extended data (Rajesh, 2021).Meanwhile, soil moisture and available water content (AWC) at each soil-phase were measured using a theta probe (Stevens Water Monitoring Systems Inc, S/N 238825) and pressure plate membrane apparatus (SOILMOISTURE Equipment Corp, Model #1500F2), respectively.

Statistical analysis
Descriptive statistics and principal component regression analysis of soil and plant parameters were carried out in SPSS v.16.An assessment of the normality of the data is pre-requisite for PCA.A Z-scale test was applied for normality test using skewness and kurtosis (for n < 300).The distribution is approximately normal if skewness and kurtosis are between -1 and 1.The right and left skewed data (Table 1 and Table 2) were subjected to logarithmic and square root transformation to improve the normality.These transformed variables were subjected to a PCA with varimax rotation (Ayoubi et al., 2009;Cox et al., 2003;Shukla et al., 2004b).The descriptive statistics of physico-chemical properties of soil and plant growth parameters are presented in Table 1 and Table 2, respectively.

Multivariate analysis
PCA was used as a data reduction technique.The independent variables such as soil properties (soil pH, EC, CaCO 3 , OC, available N, ) and plant growth parameters (plant height, number of branches per plant, LAI, number of pods at harvest stage) were subjected to a PCA; variables with factor loadings greater than the measured Kaiser-Meyer-Olkin (KMO) values, were chosen as highly correlated variables from the derived principal components (PCs) with eigenvalues greater than one (Suryanarayana & Mistry, 2016).The KMO test is a measure of sampling adequacy and factorability.The KMO tests whether the partial correlations within the data are close enough to zero to suggest that there is at least one latent underlying factor among variables (Vijayamohanan Pillai & Asalatha, 2020).As a KMO value of < 0.5 is unacceptable (Hair et al., 2003), the results of the PCA with a KMO > 0.5 loadings indicate that the chosen correlation matrix was appropriate for factor analysis.
The Kaiser Meyer Olkin (KMO) test is given by; Equation 1: The eigenvalue is the variance explained by a component or factor and is denoted by lambda.Equation 2: Where, A 2 ik is the factor loading for variable i on component k, and n is the number of variables.A low eigenvalue represents a lower contribution to the variance in the analyzed set of variables.The component matrix table shows the component loadings, which represent the correlations between the variables and the components.As a rule of thumb, the larger the size of the component loading for a variable, the more significant the variable is in the interpretation of the component (Nargundkar, 2005).The first component is generally more highly correlated with the variables than the second components, and so on.A varimax rotation enhances the interpretability of the uncorrelated components; therefore, only the outcomes of the rotated component matrix are presented in our results.

Linear principal component regression (LPCR) analysis
The derived PCs were subjected to a stepwise linear regression analysis, which is an alternative to multiple regressions analysis.In LPCR, instead of regressing the dependent variables on the explanatory variables directly, the principal components of the explanatory variables were used as regressors, each time removing the weakest correlated variable.The highly correlated and significant soil and plant independent/explanatory variables from each PC were fitted to the dependent variable (pigeon pea crop yield).Further linear equations were drawn separately.
PCA & LPCR to assess the influence of soil properties on pigeon pea yield Seven components were extracted with total cumulative variances of 82.30% and 80.75% during 2018 (Table 3) and 2019 (Table 4), respectively.The principal component 1 (PC1) in each site year was identified as soil available nutrient content with high loadings of K 2 O (0.843 and 0.764, respectively), Mn (0.870 and 0.882 respectively), Ca (0.845 and 0.900), and Mg (0.835 and 0.919 respectively).The presence of these adsorbed cations on negatively charged clay surfaces (supported   2) has inferred that there was no sufficient soil moisture to dissolve the soil nutrients to make it readily available to pigeonpea plant when compared to the rains of 2019-20 (Figure 3).In 2018, PC2 was identified as humification and vegetative growth with high loadings of OC and available N. The high loading of EC during 2018 may be due to the retention of dissolved salts associated with low precipitation.In 2019, because of higher precipitation levels (Figure 3) the results of PC2 converged towards enhanced calcification with high loadings of CaCO 3 (0.793) due to dissolution of CaCO 3 in the presence of CO 2 (Neina, 2019) which is normally found at ten times greater levels in soil (0.3 % CO 2 ) when compared to the CO 2 concentration (0.03%) in the atmospheric air above the soil.Further, PC2 in site year 2 was also identified as qualitative vegetative growth with high loadings of Cu (0.764), which enhances plant photosynthesis; this was also supported by an increased number of branches (0.595) per plant at the time of harvest.PC3 in 2018 and 2019 was identified as improved enzyme activity and pod formation stage, which was supported by high loadings of Fe (0.919 and 0.889), Zn (0.846 and 0.735) and number of pods (0.736 and 0.605) at harvest.
In 2018 (site year 1), PC4 was identified as root proliferation stage, with high loadings of P 2 O 5 (0.801) and surface soil moisture (0.727).Root interception of inorganic phosphorous increases with root proliferation.The diffusion of P 2 O 5 from the soil through the roots increases with increased moisture content of dry soil (Lambers et al., 2006).Low rainfall (Figure 2) events during critical growth stages of pigeon pea (September 2018 to December 2018), led to dry soil and reduced the dissolution of readily available (water-soluble) nutrients, which are required for plant growth.During the drought, when soil   moisture slightly increased from an uneven and low precipitation pattern during November and December 2018, the plant enhanced its root proliferation in the presence of high available soil P 2 O 5 , in search of soil nutrients (Basu et al., 2016;Peret et al., 2014).In 2019 (site year 2), PC4 was identified as humification with high loadings of EC (0.838), OC (0.558) and N (0.557) suggesting activation of humification (Doni et al., 2014).In site year 1, PC5 was identified as chemical changes in soil, and soil aeration supported by high loadings of soil pH (0.752) and silt (0.674).During site year 2, in PC5, soil ESP (-0.657) and silt (-0.583) showed a high negative factor loading, however available surface moisture (0.754) exhibited a high positive factor loading; this implies that when in water, dissociated calcium cations from CaCO 3 in the presence of CO 2, have replaced sodium ions and adsorbed on the soil colloids (van de Graaff & Patterson, 2001).In site year 1, PC6 had high negative factor loadings of CaCO 3 (-0.781)and positive factor loadings of available boron (0.788), which are attributed to plant cell wall synthesis and root nodule formation for N 2 fixation in leguminous crops.Growing plant cells responds well to boron for dividing and synthesizing cell walls when compared to growth-limited plant cells, in which the synthesis of primary cell walls is negligible (Fleischer et al., 1998).In leguminous plant roots, the development of symbiosis with soil Rhizobium bacteria depends on the concentration of available soil boron and calcium and that both nutrients are essential for nodule structure and fixing atmospheric nitrogen in the plant roots (Redondo-Nieto et al., 2003).
As CaCO 3 is a source of calcium required by the plants during critical growth stages, the dissolution of CaCO 3 in soil will be very rapid in the presence of carbon monoxide (CO), (Gallagher & Breecker, 2020), therefore the negative factor loading of CaCO 3 was observed in PC6 of site year 1.In PC6 of site year 2, high factor loadings of available P 2 O 5 (-0.715) and sulphur (0.719) were found, whereas in PC7 of site year 1, available copper (0.682) and sulphur (-0.688) had high loadings associated with enhanced enzyme activity essential for nodule formation and nitrogen fixation in the leguminous plant roots (Weisany et al., 2013).PC7 of site year 2 showed a high negative factor loading of available boron (-0.726).Legume crops required more amount of boron compared to most field crops as boron plays a vital role in the development of reproductive organs, pollen germination, flower and fruit setting.Boron being a required trace element, there is a narrow gap between deficiency and toxicity in soil-plant systems (Chatterjee & Bandyopandhyay, 2017).Therefore, one spray at the initiation of the reproductive phase is sufficient for optimum flowering and pod yield (Subasinghe et al., 2003); without initial boron spray, plant uptake will deplete boron in the soil, hence the factor loading in PC7 being negative for boron.
All seven PCs of site year 1, when subjected to stepwise linear regression with crop yield, explained the variance with an R 2 = 0.68 (p<0.01).The F test of ANOVA was significant at the 1% level.The coefficients of PC3 and PC4 were significant at the 1% level independently, whereas PC7 was significant at the 5% level.Therefore, the variables explaining PC3, PC4 and PC7 such as Fe, Zn, number of pods at harvest, available P 2 O 5 , soil moisture, and copper significantly influenced the yield (Equation 3, Table 5).Available Zn and P 2 O 5 help in seed formation, whereas available soil moisture supports crop growth, which was observed in terms of number of pods per plant and seed yield at the time of harvest.(Subrahmaniyan et al., 2008).

Conclusion
In the present study, a systematic methodological approach was adopted, using geospatial and multivariate statistical techniques for spatial classification of soils, and to gauge the major variables influencing crop yield, which helps in deriving land-or soil-specific management interventions.
Our study revealed that the predominant yield-contributing variables in rainfed agriculture depended on a number of factors, including distribution of rainfall and prevailing land slope.During a drought year (site year 1), the availability of P 2 O 5 , Fe, Zn, Cu in the soil, and number of branches and pods determined the yield.In contrast, during a high-precipitation monsoon year (site year 2), availability of K 2 O, Mn, Ca, Mg, Fe, Zn, S, CEC, clay content, available water content and number of pods per plant were the main yield-contributing factors.Therefore, in a semi-arid region, an increase in soil moisture/ available water content to an optimum level (25-30% AWC) will help dissolve most soil nutrients, and increases CEC and nutrient uptake, thus leading to a higher crop yield.
The principal component regression analysis performs a reasonable dimension reduction and operates well with highly correlated variables.Crop growth and yield depend on multiple factors but are limited by the least-available nutrient present in the environment relative to demands of this nutrient for growth.Therefore, all the loaded PCs with a minimum of 80 % cumulative variance should be considered for principal component regression analysis.The variables which were identified from the principal component regression analysis can efficiently explain the yield variability; further cross-verification of these variables with their availability in each soil management/soil-phase unit, may help to identify limiting factors, such as land surface parameters, soil depth, slope, and available soil moisture.Interventions involving these variables would need to be applied in a site-specific way to increase crop yield.
This project contains the following underlying data: -  The study could contribute to knowledge in the area of site-specific agriculture.However, the Introduction requires thorough revision: it contains some statements that should appear in Methodology.
A table (Table 3) appears repeating information already presented in the text.Also the Table inserted under

Sathish Ayyappa
Department of Soil Science and Agricultural Chemistry, University of Agricultural Sciences, Bangalore, Bengaluru, India The title of the article is precise and straight and does match with the research findings.Abstract and keywords are well framed with-in the 300 words.Methods adopted are very comprehensive and comprises a detailed soil survey at 1:8000 scale and classifying soils to series level and further dividing each series to soil phases/management units.Use of PCA and LPCR analysis to find out influence of soil properties on pigeonpea yield at soil phase level is also an indication of site specific management for various agronomic/soil operations.The technology adopted namely remote sensing data interpretation (for soil survey and classification), various lab standard analytical techniques/methods, GIS mapping, PCA, and LPCR are Apt.Therefore, the article can be approved for indexing with the following minor corrections; Page no.4: last line of the first paragraph in the second column of the page "with standard HCl (Piper, 1966)."w letter is missing.1), the scarcer rains in subsequent months during 2018-19 (Figure 2) has inferred that there was no sufficient soil moisture to dissolve the soil nutrients to make it readily available to pigeonpea plant when compared to the rains of 2019-20 (Figure 3)." 4.

Is the work clearly and accurately presented and does it cite the current literature? Yes
Is the study design appropriate and is the work technically sound?Yes

Are sufficient details of methods and analysis provided to allow replication by others? Yes
If applicable, is the statistical analysis and its interpretation appropriate?I cannot comment.A qualified statistician is required.

Are the conclusions drawn adequately supported by the results? Yes
Competing Interests: No competing interests were disclosed.
I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.com Figure 1.Location map with soil physiography interpreted imagery of LISS IV merged Cartosat-1.
Figure 6 is a repetition of same Figure, remove!For other comments, please refer to the text.Is the work clearly and accurately presented and does it cite the current literature?Yes Is the study design appropriate and is the work technically sound?Yes Are sufficient details of methods and analysis provided to allow replication by others?Yes If applicable, is the statistical analysis and its interpretation appropriate?Yes Are all the source data underlying the results available to ensure full reproducibility?Yes Are the conclusions drawn adequately supported by the results?Yes Competing Interests: No competing interests were disclosed.Reviewer Expertise: Soil and Water Conservation, Soil Physical processes, Irrigation, Soil tillage, Cropping systems I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.Reviewer Report 18 October 2021 https://doi.org/10.5256/f1000research.56445.r94826© 2021 Ayyappa S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Table 2 . Descriptive statistics of plant growth parameters at Kalmandari Tanda-1 MWS during 2018-19 and 2019- 2020.
In both the site years "number of pods at harvest" have contributed to the yield and is the one which explains yield variability among plant growth & yield parameters Note: Plht: Plant height, Br: Number of branches, LAI: Leaf area index, DAS: Days after sowing.Inference from multivariate analysis:

Table 3 . Factor loadings of soil properties (before sowing during June 2018).
Bold typeface -Strong correlation coefficientTProbe: Surface soil moisture, Br_har: Number of branches at harvest, Pod_har:Number of pods at harvest, EC: electric conductivity, ESP: exchangeable sodium percentage, CEC: cation exchange capacity, BD: bulk density, AWC: available water content.

the Mysteries of Salinity, Sodicity, SAR and ESP in On-site Practice.
In: Proceedings of On-site ' 01 Conference: Advancing On-site Wastewater Systems.by R.A. Patterson & M.J. Jones (Eds).

The effect of fertilization on the soil is some chemical and maize yield in long-term experiments.
PhD. Dissertation.Uni.Debrecen, Debrecen,  Hungary, 1999.

confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Version 1
https://doi.org/10.5256/f1000research.56445.r157225 Page no. 12 & 13: Expansion of AWC, just below the table 4 and 5, should be changed to Available water content instead of Average water content.2.Page No. 8: Under the sub heading 'PCA & LPCR to assess the influence of soil properties on pigeonpea yield' 2 nd column of the page no. 8, 7 th line, high loadings of clay content (0.897 and 0.937) these values should be changed to (0.905 and 0.929) as per the table 4 & 5 respectively.3.Page No. 8: Under the sub heading 'PCA & LPCR to assess the influence of soil properties on pigeonpea yield' 2 nd column of the page no. 8, 9 th to 14th th line (on the other hand …..(Figure2)).Please rewrite the sentence as given below;"High clay content supported high available water content with loadings of 0.822 and 0.625 during2018-19 and 2019-20 respectively.Therefore, sand (-0.858 and -901)and bulk density (-0.927 and -0.743) are negatively correlated in PC1 of both the site years.Though the initial mean available water content is high (27.93 %) in soils during 2018-19 (Table 1.