INTRODUCTION
Eggplant (Solanum melongena L.) is one of the most popular vegetables commonly grown in many countries throughout the subtropics, tropics and Mediterranean regions as it requires a long season of variable weather to give a good yield. It is well regarded amongst the vegetables because it confers health benefits such as protection against cancer (Noda et al., Reference Noda, Kaneyuki, Igarashi, Moriand and Pacer1998), lowering blood cholesterol (Guimaraes et al., Reference Guimaraes, Galvao, Batista, Azevedo, Oliveira, Lamounier, Freire, Barros, Sakurai, Oliveira, Vieira and Alvarez-Leite2000), inhibiting type-2 diabetes and hypertension (Kwon et al., Reference Kwon, Apostolidis and Shetty2008) and source of vitamins, minerals and other useful phytochemicals (Wood, Reference Wood1988).
For successful cultivation of the crop, it is important to supply adequate amounts of essential nutrients in a balanced proportion to the soil and in appropriate manner and time (Batabyal et al., Reference Batabyal, Mandal, Sarkar, Murmu, Tamang, Das, Hazra and Chattopadhyay2016b; Sharma et al., Reference Sharma, Sharma and Sharma2005). This is generally done through the use of inorganic, organic and biological sources of nutrients and their combinations. There are fundamental differences in organic and conventional production practices (Lester and Saftner, Reference Lester and Saftner2011; Worthington, Reference Worthington2001), but limited information is available detailing how various practices influence the nutritional quality of crop, especially in terms of health-related benefits, quality of growth medium (soil), energy use pattern and economics of the production system. In fact, the ecosystem services provided by the soil in any crop production system are usually constituted by a set of chemical, physical and biological attributes known as soil quality (Elliott, Reference Elliott, Pankhurst, Doube, Gupta and Grace1994). Characterization of such soil quality requires selection of indicators sensitive to changes in management practices (Basak et al., Reference Basak, Datta, Mitran, Roy, Saha, Biswas and Mandal2016). Hence, soil quality is a necessary indicator of sustainable land management (Herrick, Reference Herrick2000).
Modern agriculture practices use substantial amounts of energy resources in the form of electricity, fossil fuels, pesticides and fertilizers for growing crops and their environmental consequences include increased emission of greenhouse gases to the atmosphere and deterioration in the ecosystem health. Efficient use of different energy inputs helps achieve optimum production and productivity of crops and contributes to economy, profitability and sustainability to rural livelihoods (Singh et al., Reference Singh, Verma and Mittal1997). Hence, we hypothesized that the nutrient management (NM) technology adopted for cultivation of the crop likely influences not only the biomass yield, but also crop quality (food value), the quality of soil and surrounding environment, energy use of the system and the overall economics of its cultivation. For a comprehensive evaluation of any NM technology, it is necessary to critically assess its influence on the above aspects and then to screen out the best management technology for sustainability of natural resources and livelihood security of farming community.
To the best of our knowledge, there has rarely been any attempt before to evaluate the NM practices usually adopted by the farmers and recommended by the development agencies for cultivation of eggplants with the above end in view. The previous works examined the effect of NM either on yield (Batabyal, Reference Batabyal2011; Choudhary et al., Reference Choudhary, Talukdar and Saikia2005) and quality (Kashyap et al., Reference Kashyap, Kumar, Maji and Kumar2014; Raigon et al., Reference Raigon, Rodríguez-Burruezo and Prohens2010; Tamang, Reference Tamang2010) of crop or on soil quality (Mandal, Reference Mandal2005) or energy balance (Batabyal et al., Reference Batabyal, Mandal and Hazra2016a; Gelfand et al., Reference Gelfand, Snapp and Robertson2010; Moraditochaee, Reference Moraditochaee2012) and profitability (Meena et al., Reference Meena, Dhaka and Jalwania2005; Verma et al., Reference Verma, Singh, Gupta and Singh2013) for eggplants or any other crop in an exclusive manner. In the present experiment, we have evaluated the NM technologies for eggplants in a comprehensive manner including all the above aspects as the goal variables.
MATERIALS AND METHODS
Experimental site
The experimental site is located in hot humid subtropics at the Central Research Farm (23°N, 89°E, 9.75 m msl), Bidhan Chandra Krishi Viswavidyalaya, West Bengal. The site received, on average, 93.7, 97.5 and 89.3 mm rainfall and had 30.1 and 20.2, 30.4 and 20.6, and 29.9 and 20.4 °C of mean maximum and minimum temperatures,76.5, 78.9 and 85.1% relative humidity, and 6.5, 6.6 and 6.1 hours sunshine during crop growing season (September to December) of 2009, 2010 and 2011, respectively (Supplementary Table S1, available online at http://dx.doi.org/10.1017/S0014479716000600). The soil is sandy loam (hyperthermic Aeric Haplaquept according to US Soil Taxonomy; Soil Survey Staff, 2003) in texture with average clay, silt and sand contents of 28.8, 32.0 and 39.1%, respectively.
Treatments and management
Fifteen NM treatments comprising sources of organic, inorganic and their combinations, and an unfertilized control (Table 1) were tested for three consecutive growing seasons (2009 to 2011) in 10×10 m plots arranged in a randomized block design with three replications. The layout, a semi-permanent one, with the same treatment structure has been in use for the last 6 years (2006–2011) for raising eggplants. For organic plots, control of insect pests, diseases and weeds was done with botanical agrochemicals. All the organics viz., VC, FYM and MOC were applied on wet weight basis with average moisture content of 30, 40 and 10% (w/w), respectively. Vermicompost used was prepared by composting cow-dung and organic waste feed stocks (at 40:60 on dry weight basis) with the help of about 3 kg of red earthworms (Eisenia foetida) per Mg of biomass for 2 months. The organics (VC, FYM, MOC) were applied 30 days before final land preparation. The doses of organics and chemical fertilizers were selected based on published data of nutrient uptake by the crop (Choudhary et al., Reference Choudhary, Talukdar and Saikia2005; Nanthakumar and Veeragavathatham, Reference Nanthakumar and Veeragavathatham2000; Sharma and Brar, Reference Sharma and Brar2008) and also considering the economics and the rates commonly followed by farmers. Healthy disease free 22-day-old seedlings of eggplants (cv. Goria) were planted at a spacing of 0.8×0.8 m. Half of N along with the full amounts of P and K of inorganic fertilizer were applied during final land preparation and the rest amount of N was broadcast in two equal splits – one at 30 and other at 55 days after transplanting of seedlings. In addition to 2–3 light winter rainfalls, about 30 cm ha−1 of water was used in each application for irrigating eggplants (0, 7, 15, 22, 30, 38, 50, 61, 72 and 85 days after transplanting) from a deep tubewell. Fully developed fruits of eggplant were harvested starting from 80 to 120 days of growth.
Table 1. Details of nutrient management treatments used in the experiment.
Biometric observation and sample collection
Total fruit yield, marketable fruit yield, average fruit weight, fruit length and fruit girth of eggplants were recorded during harvesting. Market quality attributes were mainly visual such as size, shape, colour and defects: punctures, insect wounds, diseased areas and scarring. Eggplant fruits with desired shape, size, colour and free from any abnormalities were included in marketable yield.
After recording crop biometrics, representative fruit and leaf samples were collected from each of the plot under different treatments for analysis. Five representative field moist soil samples (0–0.20 m depth) were also collected before the experiment in 2006 and after the sixth crop harvest in 2011 from each of the plots with a bucket auger. They were pooled together to make composite samples for each replication, then hand crushed, passed through a 2.0 mm sieve, stored at 4 °C, and used fresh within 24 h for estimating soil microbial biomass C (MBC). A portion of the soil samples was air dried, powdered, passed through the same sieve and used for analysis of pH, oxidizable organic C and extractable plant nutrients. Additional duplicate samples were taken from each replication using a core sampler (0.05 m in diameter, 0.08 m in length) for measuring bulk density (BD) of the soil following the method described by Blake and Hartge (Reference Blake, Hartge and Klute1986).
Chemical analysis
Representative plant samples (fruit and leaf) were processed and analysed for N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B following standard procedures (Chapman and Pratt, Reference Chapman and Pratt1961). Vitamin C content in the fruit was estimated on the day of harvest to counteract the instability effect, by indophenols method as outlined by Nielsen (Reference Nielsen1998) following AOAC (AOAC, 1995). Total phenols were determined by macerating 1 g fresh fruit tissue in 25 mL of 95% ethanol followed by boiling for 30 minutes in a water bath and then centrifuging the extract at 15 000 rpm for 20 minutes. The supernatant liquid was evaporated to dryness in a water bath and subsequently the residue was dissolved in 25 mL of double distilled water and used for determination of phenol content. The estimation of phenol was carried out with the Folin–Ciocalteau reagent using a catechol standard by measuring the absorbance at 650 nm following the method of Bray and Thorpe (Reference Bray and Thorpe1954). Soil samples were analysed for pH, oxidizable organic C (Walkley and Black, Reference Walkley and Black1934), alkaline KMnO4 extractable N (Subbaiah and Asija, Reference Subbiah and Asija1956), sodium bicarbonate extractable P (Olsen et al., Reference Olsen, Cole, Watanabe and Dean1954), 1.0 M NH4OAc (pH 7.0) extractable K, Ca and Mg (Sparks et al., Reference Sparks, Page, Helmke, Loeppert, Soltanpour, Tabatabai, Johnston and Summer1996), DTPA extractable Fe, Mn, Cu and Zn (Lindsay and Norvell, Reference Lindsay and Norvell1978) and hot-CaCl2 extractable B (Parker and Gardner, Reference Parker and Gardner1981).
The MBC was measured following chloroform (CHCl3) fumigation and extraction with 0.5 M K2SO4 (Vance et al., Reference Vance, Brookes and Jenkinson1987). Organic manures (VC, FYM, MOC) were analysed for their nutrient contents (Supplementary Table S2) by digesting the samples with di-acid (HNO3:HClO4 at 3:1) mixture. The clear digest was used for estimation of P, K, Ca, Mg, Fe, Mn, Cu, Zn and B following standard procedures (Page et al., Reference Page, Miller and Keeney1982). Lignin and cellulose contents were measured by the acid detergent fibre-permanganate method of van Soest and Wine (Reference Van Soest and Wine1968). Polyphenols were extracted at 80 °C with an aqueous methanol solution (1:1 water methanol ratio) and measured calorimetrically in the presence of Folin–Denis reagent (King and Heath, Reference King and Heath1967). Total N and organic C contents of soil and manures were estimated using a CHN analyser (Thermo Elemental Analyzer, Model Flash 2000).
Crop quality assessment
Crop quality was assessed using linear indexing technique to integrate different quality parameters. For purpose of human nutrition, N, P, Ca, Mg, Fe, Zn, B, vitamin C, β-carotene and antioxidant content in eggplant fruits were taken into consideration. Values of each of the quality parameter for different NM treatments were divided by the highest observed value of the respective parameter so that the highest observed value received a score of 1. Scores of each of the selected quality parameters thus obtained were summed up to get crop quality index (QI) for each treatment. The higher the index score better is the crop quality for human nutrition.
Soil quality assessment
Soil quality index (SQI) was calculated using the method of Andrews and Carrol (Reference Andrews and Carrol2001) as modified by Basak et al. (Reference Basak, Datta, Mitran, Roy, Saha, Biswas and Mandal2016). The first part aims at reducing the dataset into a minimum, whereas the second part aims at marrying the minimum dataset (MDS) into a QI for each treatment. The dataset (total of 14 attributes) was reduced to a MDS of soil quality indicators through a series of multivariate statistical methods. Only variables with significant differences between treatments were chosen for the MDS formation. Standardized principal component analysis (PCA) was performed with those significant variables and reduced the redundancy summing up simple correlation values amongst the screened variables. Representation of the screened variables towards the goal variables (fruit yield and crop quality) was validated through computing multiple regressions. Once confirmed, the screened variables were integrated through linear weighted scoring technique into a soil QI for each technology/treatment. Higher index scores were assumed to mean better soil quality.
Carbon balance study
Carbon balance in soil was calculated by measuring carbon addition through the three organic sources and their different combinations for the last 6 years and the left over carbon in soil as measured by the changes in the magnitude of soil organic carbon (SOC) stock in post-harvest soil under each treatment over that in initial soil. The SOC stock under each NM treatment was computed by multiplying the organic carbon content (g kg−1) value with the corresponding BD (Mg m−3) and depth (m) of soil. Efficiency of such SOC from different sources for aggrading SQI was calculated by dividing the change in SQI (∆SQI) of post-harvest soil under different treatments over that of initial soil [∆SQI=SQI(Post harvest soil)−SQI(Initial soil)] by the amount of C added (Mg C ha−1) to soil through those organics for the last 6 years.
Energy analysis
For calculating energy balance, inputs (seeds, fertilizers, plant protection chemicals, farm machineries for tillage and irrigation and human labour) and output (total fruit yield) were converted to energy values by multiplying with corresponding energy coefficients or equivalents (Table S2) (Panesar and Bhatnagar, Reference Panesar and Bhatnagar1987). Tillage operation was done with the help of a tractor (60 hp) drawn rotary tiller and cultivator. The loading factor (ratio of tractor drawbar power to engine power) for different tractor operated machinery was determined from actual fuel consumption and the specific fuel consumption (supplied by the manufacturer) of the tractor. The average loading factors of rotary tiller and cultivator were 60 and 40%, respectively, for inorganic and integrated plots, whilst the corresponding values for organic plots were 50 and 30%, respectively. The crop was irrigated with deep 3-phase tube-well (10 hp; induction motor). Energy requirement for tillage and irrigation was calculated based on consumption of fuel and electricity, respectively. In the case of fertilizers, seeds and plant protection chemicals energy required for their manufacture was taken into account. All other operations such as transplanting, fertilizer application, spraying of chemicals, intercultural operations and harvesting were performed by human labour and a value of 1.78 MJ h−1 was used. Whilst calculating energy balance, we have considered only the factors supplied and controlled by farmers. In agreement with Rathke et al. (Reference Rathke, Wienhold, Wilhelm and Diepenbrock2007), solar energy was not considered. Energy removed/captured from soil in the form of plant nutrients and in terms of increase or decrease in soil organic matter were not included in the energy balance (Zentner et al., Reference Zentner, Lafond, Derksen, Nagy, Wall and May2004). We evaluated energy efficiencies in terms of net energy (NE) – also known as energy gain – and energy output/input ratio (O/I).
Economic analysis
We computed total cost of cultivation taking into account the cost of land preparation, irrigation and cost of inputs, which included fertilizers, seeds, plant protection chemicals and labour used for their application and for other operations. The unit costs for different operations employed, inputs used and output received/produced were given in Supplementary Table S3. Cost of land was not included in the study because majority of the farmers own their land. The analyses of net return (NR), benefit/cost ratio (BCR) and marginal rate of return (MRR) were performed.
Evaluation of NM technologies
We compared the relative strength/benefits of each of the 14 NM technologies (except control) by employing non-parametric evaluation of regression factor scores through PCA using fruit yield, crop quality as well as environmental criteria (SQI), energy efficiency (EG) and profitability of cultivation (MRR) as goal variables. In this screening technique, all components corresponding to eigenvalues more than one have been considered. The relative size of the eigenvalue associated with a particular component indicates the relative contribution of the concerned component to the total variance of original data set. Regression scores were then ranked from 1 to 14; 1 was the highest score and the least one was 14. The ranksum result of all ranked components revealed the best treatment to be adopted.
Statistical analysis
Windows-based SPSS Statistical (v.21, Chicago IL, USA) programme was used for analysis of variance and to determine significance of treatments in biomass yield, nutrient concentration, crop quality attributes, energy variables, economics of cultivation and soil properties. Although some of these parameters varied across years, year × treatment interactions were non-significant (P < 0.05) in most cases. Therefore, data were pooled and presented across years, excepting the total fruit yield, marketable fruit yield and NR as they showed significant variation across the years. Duncan's Multiple Range Test was used to compare treatment means. Simple correlations were computed along with PCA to evaluate relationships between the response variables and to screen the performance of the NM technologies using the same statistical package.
RESULTS
Yield and yield related traits
Total and marketable fruit yield of eggplants varied significantly across the years, but year × treatment interaction was non-significant (Table 2, Figure 1). Average fruit weight, fruit length and fruit girth were influenced neither by year nor by year × treatment. This indicated that yield and yield related traits of eggplants were consistent over the years. Application of different levels and sources of nutrients significantly (P < 0.05) influenced the yield of eggplants (Table 2). When compared to the control, organic, inorganic and integrated sources of nutrients resulted in 64, 69, 112, 54, 71 and 116% increase in total and marketable fruit yield, respectively. On average, marketable yield contributed only 49% of the total fruit yield. Amongst the organics, MOC excelled over the others both for the total and marketable fruit yield. Again, the average fruit weight was higher with integrated (158 g) and inorganic sources (129 g) as compared to organics (109 g) and control (73 g). Similar trends were also obtained with respect to average fruit length and fruit girth.
Table 2. Effect of different nutrient management technologies on yield and yield attributing parameters of eggplants.
NPK, N–P-K at 100–22–42 kg ha−1; NPK^, 150% of NPK; VC, vermicompost; FYM, farmyard manure; MOC, mustard oil cake; numbers followed by VC, FYM and MOC indicate dose in Mg ha−1; figures followed by different lowercase letters within a column are significantly different between treatments at P ≤ 0.05 by Duncan's multiple-range test (DMRT); *, ** and *** = significant at P < 0.05, 0.01 and 0.001, respectively; NS = not significant.
Figure 1. Variations in total fruit yield (TFY), marketable fruit yield (MFY) and net return (NR) of eggplant cultivation by year under different nutrient management technologies.
Nutrient content
Elemental composition of eggplant fruits varied significantly (P < 0.05) with the sources and levels of nutrients applied (Table 3). On average, it had higher concentrations of macro- (N), secondary- (Mg) and micro- (Fe, Mn, Zn) nutrients when grown with integrated sources of nutrients as compared to sole inorganic and organic sources. Interestingly, organically grown fruits recorded higher content of P, K, Ca and B over the others. The ratios of N, P, K, Ca, Mg, Fe, Mn, Cu, Zn and B concentration in fruit to leaf across the treatments were 0.71, 0.81, 1.38, 0.13, 0.16, 0.36, 0.29, 0.60, 0.55 and 0.65 respectively, with values being higher under integrated treatments. This indicated that fruits, in general, had less nutrients than leaf, excepting Ca.
Table 3. Effect of different nutrient management technologies on nutrient concentration in eggplants.
NPK, N–P–K at 100–22–42 kg ha−1; NPK^, 150% of NPK; VC, vermicompost; FYM, farmyard manure; MOC, mustard oil cake; numbers followed by VC, FYM and MOC indicate dose in Mg ha−1; figures followed by different lowercase letters within a column are significantly different between treatments at P ≤ 0.05 by Duncan's multiple-range test (DMRT); *, ** and *** = significant at P < 0.05, 0.01 and 0.001, respectively; NS = not significant.
Crop quality parameters and quality index
NM practices significantly (P < 0.05) influenced the total phenol and vitamin C contents in the fruits with values varying from 20.4 to 50.3 and 4.8 to 15.1 with a mean of 37.8 and 8.6 mg (100 g)−1 on fresh weight basis, respectively (Table 4). On average, conjoint application of organic and inorganic sources of nutrients produced fruits with higher concentration of vitamin C than their individual application; whilst the total phenol content was higher in organically raised fruits. Amongst organics, fruits of eggplants grown with MOC and FYM had the highest amount of total phenol [48.5 mg (100g)−1] and vitamin C [6.9 mg (100g)−1] content, respectively.
Table 4. Effect of different nutrient management technologies on crop quality and its quality index.
NPK, N–P–K at 100–22–42 kg ha−1; NPK^, 150% of NPK; VC, vermicompost; FYM, farmyard manure; MOC, mustard oil cake; numbers followed by VC, FYM and MOC indicate dose in Mg ha−1; figures followed by different lowercase letters within a column are significantly different between treatments at P ≤ 0.05 by Duncan's multiple-range test (DMRT); *, ** and *** = significant at P < 0.05, 0.01 and 0.001, respectively; NS = not significant.
QI of the fruits for human consumption varied significantly (P < 0.05) with the sources and levels of nutrients applied (Table 4). On average, fruits with integrated treatments had higher values of QI followed by those with only organic and inorganic treatments. Amongst organics, VC produced the best quality fruits (QI 6.7); whereas MOC produced the worst (QI 6.1).
Soil quality
Application of different levels and sources of nutrients for 6 years caused significant (P < 0.05) variations in soil properties (Table 5). Organics and their integration with inorganics resulted in decreases in soil pH and BD. MBC content of the residual soils varied from 38 to 318 mg kg−1, which constituted, on average, only 1.6% of total organic C (TOC) content of the soils. Higher values of MBC were recorded with organic and integrated treatments as compared to inorganic ones. On average, available N, P2O5 and K2O contents of the soils were higher with integrated treatments than those with organic and inorganic ones. The available Ca and Mg contents of the soils ranged from 3674 to 7034 and 672 to 1183 kg ha−1, respectively. The values of Ca, in general, were higher under organic and integrated treatments over the inorganic ones; whilst those of Mg were comparable amongst the treatments. Availability of micronutrients (B, Fe, Mn, Cu and Zn) was more in soils under organics and integrated NM practices compared to inorganics alone.
Table 5. Effect of different nutrient management technologies on the changes in soil properties and soil quality index after 6 years of eggplant cultivation.
NPK, N–P–K at 100–22–42 kg ha−1; NPK^, 150% of NPK; VC, vermicompost; FYM, farmyard manure; MOC, mustard oil cake; numbers followed by VC, FYM and MOC indicate dose in Mg ha−1; figures followed by different lowercase letters within a column are significantly different between treatments at P ≤ 0.05 by Duncan's multiple-range test (DMRT); *, ** and *** = significant at P < 0.05, 0.01 and 0.001, respectively; NS = not significant.
The values of SQI in residual soils were always higher than those in initial one (Table 5). The changes in SQI over the initial soil were higher with organic treatments than those with inorganics or integrated ones and the relative order of increase in SQI was: organic (5.262) > integrated (5.094) > inorganic (3.530). Of the three organic amendments, FYM was the most efficient in improving soil quality followed by VC and MOC. When such effect of each of these organics in combination with inorganic was compared, FYM again excelled over VC and MOC.
Soil organic C balance
Growing eggplants without the external sources of nutrients (control) caused low TOC (Figure 2). Carbon supplementation through organics, on average, increased the TOC stock of the soils by 29% over its initial content. Only inorganics did not show any significant change (+0.6%) in organic C content but when integrated with organics caused an increase of 12%. The organic treatments, which included FYM, such as FYM20, FYM10+VC5 and MOC1.6+FYM5 caused increase in TOC as much as 57, 49 and 20%, respectively, over that in the initial soil.
Figure 2. Soil organic C (SOC) stock and C balance in soil after 6 years of eggplant cultivation under different nutrient management technologies.
Energy balance
Energy efficiency parameters such as NE and O/I showed significant (P < 0.05) variations amongst the treatments (Table 6). On average, NE was higher with integrated (89.7 GJ ha−1) and inorganic treatments (74.5 GJ ha−1) than that with organic ones (68.1 GJ ha−1). The average values of O/I ratios, however, followed the reverse order with organics having the highest efficiency ratio (6.8). Amongst the organics, O/I value was higher with FYM20 and VC10.
Table 6. Influence of nutrient management technologies on energy variables and economics of eggplant production.
NPK, N–P–K at 100–22–42 kg ha−1; NPK^, 150% of NPK; VC, vermicompost; FYM, farmyard manure; MOC, mustard oil cake; numbers followed by VC, FYM and MOC indicate dose in Mg ha−1; figures followed by different lowercase letters within a column are significantly different between treatments at P ≤ 0.05 by Duncan's multiple-range test (DMRT); *, ** and *** = significant at P < 0.05, 0.01 and 0.001, respectively; NS = not significant; Net energy = energy output – energy input; Net return = gross return – total cost; Benefit/cost ratio = gross return/total cost; Marginal rate of return = net return over control/total cost over control; US$1 = INR 66.
Economic analyses
Across the years, total costs of growing eggplants varied significantly (P < 0.05) amongst the treatments. The year effect was significant for NR only. Profitability parameters such as NR, BCR and MRR showed wide variations amongst the treatments with values ranging from US$1,073 to 4,750, 1.7 to 3.3 and 1.1 to 10.9, respectively (Table 6). On average, integrated and inorganic treatments had higher values of BCR and MRR over organic ones but the NR was higher with organic treatments than sole inorganic. Amongst integrated sources, FYM5+NPK and FYM5+NPK^ recorded higher NR (US$3,787 and 3,679, respectively) and MRR (10.1 and 8.9, respectively).
DISCUSSION
Eggplant yield
The annual variations in the total fruit and marketable fruit yield of eggplants, as observed herein, were attributed to the fluctuations in weather parameters, particularly rainfall, relative humidity and sunshine hours during the crop growing season (Table S1). Such variations in weather conditions resulted in an increase in incidence of disease and pest attack producing a year effect consistent over all the treatments (Figure 1). Low relative humidity in 2009 caused an increase in the incidence of eggplant shoot and fruit borer (Leucinodes orbonalis Guene) resulting in lower fruit yield when compared to 2010. Again, occurrence of winter rainfall and prevailing of high relative humidity coupled with low sunshine hours during 2011 led to an increase in the incidence of fruit rot disease caused by Phytophthora nicotianae, resulting in a reduction in fruit yield.
The sources of nutrients had significant (P < 0.05) influence on the conversion of plant biomass to fruit formation (economic yield). Integrated and inorganic sources excelled over organics not only for producing total fruit yield but also for marketable yield, which is preferred both by the customers and farmers. A slow release and limited supply of nutrients, particularly nitrogen – a key element for optimum nutrition to produce high yield and quality fruits of eggplants – from organic sources might be the reasons for the above observations (Kage et al., Reference Kage, Alt and Stutzel2003). Total amount of nitrogen applied through any of the organics or combination of organics varied from 90 to 151 kg ha−1. Assuming one-third of the total nitrogen of the organics becomes available to the current crop on decomposition, the amount of nitrogen actually available to eggplants from those sources was not more than 50.3 kg ha−1 as compared to 125 and 145 kg ha−1 available from inorganic and integrated sources, respectively. Susceptibility to disease and pest infestation (which ultimately contribute to marketable yield) may be a varietal character but because of inadequate availability of the nutrients from organic sources, eggplant fruits failed to mature with desired shape and size with organic sources. Proper fruit formation possibly requires a sustained and adequate supply of nutrients, which cannot be provided by the organics sufficiently. All these explained poor performance of the organics in producing marketable yield of eggplants. The superiority of MOC amongst the organics was due to its higher contents of N, P and K and organic C (Table S2), causing increased vegetative growth and consequently yield of eggplants.
Nutrient content
There was a higher content of mineral nutrients in fruits raised with the inorganic and integrated sources of nutrients over the organic sources. This was in agreement with the observations of Islam et al. (Reference Islam, Karim, Jahiruddin, Majid, Miah and Islam2013). Higher concentration and uptake of nutrients particularly N, Mg, Fe, Mn and Zn by fruits of eggplant under integrated treatments indicated that conjoint application of organic and mineral fertilizer is able to supply those nutrients in steady rate and in balanced amount to induce fruit formation. Interestingly, organics facilitated mobilization and subsequent enrichment of P, K, Ca and B in eggplant fruits as compared to inorganic and integrated treatments. This might be related to the higher affinity of these elements for getting complexed with organics and subsequently maintaining in available form in soils (Herencia et al., Reference Herencia, Ruiz, Morillo, Melerol, Villaverde and Maqueda2008).
Crop quality
There was significant (P < 0.05) variations in N, P, Ca, Mg, Fe, Zn, B, total phenol and vitamin C content in fruits under different treatments. They, in turn, caused variations in crop QI. Similar variations in total phenol and vitamin C contents in fruits of eggplant with different NM practices were previously reported (Kashyap et al., Reference Kashyap, Kumar, Maji and Kumar2014). Higher vitamin C content with FYM treatment might be due to good growth of plants resulting from higher assimilation of micronutrients made available in soils due to its (FYM) decomposition. This increased availability of micronutrients in plants can also increase the activity of ascorbic acid oxidase enzyme and thus its concentration with FYM treatment (Malik et al., Reference Malik, Chattoo, Sheemar and Rashid2011). Our values of vitamin C content of fruits were within the range reported by Kashyap et al. (Reference Kashyap, Kumar, Maji and Kumar2014). Again, there was higher QI of fruits raised with VC because its water soluble components such as humic acid, growth regulators, vitamins and micronutrients increased the availability of plant nutrients in soils resulting in increased growth, higher yield and better quality produce (Atiyeh et al., Reference Atiyeh, Lee, Edwards, Arancon and Metzger2002).
Soil quality
Application of organic amendments and their integration with inorganic fertilizers resulted in a decrease in soil pH and BD and this was expected as applied organic materials on decomposition produce organic acids and decrease soil pH, whilst organics form soil aggregates and decrease bulk density (Bandyopadhyay et al., Reference Bandyopadhyay, Saha, Mani and Mandal2010). Out of the 10 nutrient elements analysed, availability of almost all the elements in soil was higher in organic and integrated treatments than in the inorganic ones. This was due to mineralization and subsequent release of those elements contained in the organics (VC, FYM and MOC). Soil MBC, accounted for only 0.6–2.8% of total organic C, is more dynamic and fluctuates more with soil management practices than the total organic C (Smith and Paul, Reference Smith, Paul, Ballag and Stotzky1990). Application of organics alone or with inorganics provided a more favourable environment for rapid microbial growth and caused a greater increase in MBC in these soils (Moscatelli et al., Reference Moscatelli, Lagomarsino, Marinari, De Angelis and Grego2005). Therefore, management practices that include incorporation of organic matter into soil increase the biological activity. We observed a larger increase in MBC with FYM compared to VC or MOC possibly due to a higher presence of decomposition resistant fractions in the former compared with the latter two (Table S2). Such higher MBC content in soil with FYM was also reported in alluvial soil of the hot humid subtropic in eastern India under rice based cropping systems (Majumder et al., Reference Majumder, Mandal, Bandyopadhyay, Gangopadhyay, Mani and Kundu2008). The lowest values of available nutrients and MBC in the control were likely caused by a severe depletion of nutrients on continuous cropping without any external sources of nutrients.
The observed higher SQI values in residual soils over the initial one indicated that the application of organics along with and without chemical fertilizers over 6 years helped to upkeep soil quality by improving soil organic C, MBC, BD and other available nutrients. Such improvement showed positive relationship (r = 0.657; P = 0.020) with the amount of C added to the soil through different organics. However, there were considerable variations in the magnitude of improvement in SQI per unit organic C applied through the three sources used. For example, VC10 caused the highest changes (0.580) per unit of its C addition, followed by MOC5 (0.444) and FYM20 (0.180). This indicated that both quantity and quality of the organics were equally important in improving the SQI.
There was depletion in organic C content in soils due to cultivation of eggplants and similar loss of oxidizable OC content in soil due to cropping are not uncommon in subtropical regions because of high temperature (Bhattacharyya et al., Reference Bhattacharyya, Pal, Chandran, Mandal, Ray, Gupta and Gajbhiye2004). Cropping needs plowing that disturbs the distribution and stability of soil aggregates and exposes soil organic C to rapid oxidation (Mandal et al., Reference Mandal, Majumder, Adhya, Bandyopadhyay, Gangopadhyay, Sarkar, Kundu, Gupta Choudhury, Hazra, Kundu, Samantaray and Misra2008) resulting in the observed depletion in control TOC stock. Again, a large amount of plant residual (root biomass and rhizo-deposition) C was left over soil under integrated treatments owing to an increased yield (Manning and Renforth, Reference Manning and Renforth2013) and enrichment in soil organic C content. Loss of C from soil due to oxidative processes of plowing for cultivation was just compensated and more than compensated for by the addition of C into soil through increased below ground root biomass under inorganic and integrated treatments, respectively.
Out of 1.8 to 23 Mg C ha−1 added through organic amendments, only 0.4 to 8.6 Mg could be stabilized after 6 years of experimentation (Figure 2). This indicated that around 43% of the added C was stabilized into TOC under organic and integrated sources. Amongst the treatments compared, application of FYM either alone or in combination with other organics (FYM20, FYM10+VC5, MOC1.6+FYM5) or with inorganics (FYM5+NPK, FYM5+NPK^) could stabilize a higher fraction (more than 37%) of added C compared to other treatments. This indicated that the C applied through FYM was more resistant than that applied through VC or MOC. This was due to higher C/N ratio, lignin and polyphenol contents in FYM than those in MOC and VC (Table S2). The higher content of lignin and polyphenol in FYM led to the formation of stable complexes with proteins of plant origin and thus made the FYM C more resistant to decomposition than the VC and MOC (Tian et al., Reference Tian, Kang and Brussard1992). All these indicate that both the quality and quantity of C inputs into soil have an important role in building up TOC. We also observed that stabilization of applied C into SOC was increased with increasing levels of organic C added through different sources. The strong linear relationship between the changes in SOC stock (C sequestration) and the cumulative C inputs to the soils over the years (y = 0.419×+0.010; R2 = 0.910; P = 0.001; Figure 2) suggest that even after 6 years of C additions at a reasonable rate (1.20 Mg ha−1 yr−1), the soils are still unsaturated in their capacity for storing C and, therefore, have great potential for further C sequestration.
Energy balance
Higher energy gain under integrated and inorganic treatments over organics was due to higher fruit biomass yield that led to more output energy. Again, higher magnitude of energy yield per unit of input energy level (O/I ratio) under organic system except MOC treated plots could be attributed to lower input energy associated with the system. Similar high energy efficiency in organic systems was reported by others (Batabyal et al., Reference Batabyal, Mandal and Hazra2016a; Moreno et al., Reference Moreno, Lacasta, Meco and Moreno2011). The O/I ratio measures the environmental effects of crop production and is suitable for use in determining optimum input levels from an ecological point of view (Rathke et al., Reference Rathke, Wienhold, Wilhelm and Diepenbrock2007). However, with increasing trend of consumption of food and energy in the world, the energy gain is a much more relevant parameter to determine the efficiency of crop production systems. Considering this, VC3+NPK^ and FYM5+NPK were found the most energy efficient technologies for raising eggplants.
Economic analyses
There was significant year to year variation in NR for eggplant production and this was related to the observed annual variations in fruit yield particularly marketable fruit yield (Figure 1). The values of all the profitability parameters viz., NR, BCR and MRR were higher with integrated NM systems over the organic ones indicating that cultivation of ‘organic’ eggplants was less profitable in spite of its higher market cost (30% more). In fact, most of the organic treatments recorded lower MRR, excepting FYM20 (8.9) that had higher NR (US$ 3623 ha−1) and BCR (2.9) values. This lower MRR with the organic system was mainly due to their higher input cost and low economic yield. On the other hand, despite their high cost, inorganic and integrated treatments were profitable due to their higher fruit yield. This was further confirmed by high MRR with inorganic (NPK and NPK^) and integrated (particularly FYM5+NPK and FYM5+NPK^) treatments because of lower cost of inputs and higher NR. These four NM technologies and FYM20 will be profitable to the farmers of the subtropical alluvial region for growing eggplants as MRR determines the benefit that a farmer would expect by switching from his existing technology to another one (Evans, Reference Evans2005).
Technology screening
The results of PCA and subsequent regression factor scoring of principal components using marketable fruit yield, crop quality, soil quality, energy efficiency and economic profitability showed that the highest rank (rank sum score 6) was associated with the treatment VC3+NPK^(Table 7). TheVC3+NPK^was thus the best amongst the NM technologies compared for growing eggplants. The superiority of VC3+NPK^ was mainly associated with its higher marketable fruit yield (12.27 Mg ha−1), economic return (MRR 8.7), energy efficiency (NE 113.8 GJ ha−1) and greater improvement in soil quality (∆SQI 3.651) and crop quality (QI 6.8) for human nutrition. Amongst the organics, FYM20 was superior over the others because of higher marketable fruit yield (7.23 Mg ha−1) and the highest values of ∆SQI (4.144), NE (87.9 GJ ha−1) and MRR (8.9).
Table 7. Regression factor scores of treatment evaluation for cultivation of eggplants.
*PC stands for principal component.
CONCLUSIONS
Out of the 15 NM technologies evaluated, cultivation of eggplants with conjoint application of 150% NPK (150–33–63 kg N–P–K ha−1) and 3 Mg ha−1 of vermicompost was found to be the best for high fruit yield, better fruit quality and ecological health of the production system. Only organic NM practice may not be sustainable for eggplants production in hot, humid subtropical India.
Acknowledgements
The authors are thankful to Dr L. N. Mandal, former Professor of Soil Science, Bidhan Chandra Krishi Viswavidyalaya, West Bengal, India for his critical comments on first draft and offering valuable suggestions in strengthening the manuscript.
Supplementary material
To view supplementary material for this article, please visit http://dx.doi.org/10.1017/S0014479716000600
