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Frontiers in Agronomy ·

Volume 8 - 2026 | https://doi.org/10.3389/fagro.2026.1946224

Sustaining hill agriculture: climate-smart integrated farming systems for tribal smallholders in Eastern Himalayas, India

  • Moloy Sarmah Baruah1
  • Mokidul Islam1*corresponding author
  • Prasanta Kumar Pathak2
  • Amulya Kumar Mohanty2
  1. 1Department of Agricultural Research and Education (DARE), ICAR Research Complex for NEH Region, Umiam, India
  2. 2Department of Agricultural Research and Education (DARE), ICAR-Agricultural Technology Application Research Institute, Zone-VII, Umiam, India
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Abstract

Worldwide, the division of ancestral land has reduced the size of operational farm holdings. In Northeast India, 78.9% of smallholders cultivate micro-plots plagued by resource constraints and seasonal water shortages, rendering single-enterprise agriculture unsustainable. Climate-Smart Integrated Farming Systems (CS-IFS) provide a "vertical expansion" strategy that recycles nutrients and operational inputs to economically shield smallholders from climate disruptions and land fragmentation. This study aimed to assess and validate three 0.5-hectare CS-IFS models regarding carbon dynamics, climate resilience, and sustainable livelihoods for tribal farmers in the Eastern Himalayas. A four-year field investigation (2021–2024) was conducted in Meghalaya, India, evaluating three 0.5-ha models: Poultry–Fish–Vegetable–Vermicompost (PoultryFVV), Duck–Fish–Vegetable–Vermicompost (DuckFVV), and Pig–Fish–Vegetable–Vermicompost (PigFVV). Carbon dynamics were evaluated using IPCC greenhouse gas protocols and Life Cycle Assessment (LCA). Climate resistance was measured using the Climate Resilience Adaptation Index (CRAI), while economic sustainability, profitability, and employment creation were evaluated using standard financial metrics. PoultryFVV recorded the highest methane emissions (1,536.8 kg CO 2 -eq.) and overall carbon footprint (1,674.2 kg CO 2 -eq. yr −1), whereas DuckFVV exhibited the lowest footprint (1,473.0 kg CO 2 -eq. yr −1). Carbon sequestration was consistent across models at 2.28 kg CO 2 -eq. yr −1. All models maintained high productivity resilience (92–93%) under climate stress. PoultryFVV achieved the highest adaptive capacity (0.633), overall resilience rating (CRAI > 0.67), sustainability index (SVI 0.561), and net profits (US$ 1,757.59 yr −1 with a benefit–cost ratio of 2.47), while generating 227 man-days yr −1 of employment. However, PigFVV demonstrated the highest daily economic efficiency (US$ 4.82 day −1). The study demonstrates that PoultryFVV is the most reliable, profitable, and sustainable model for climate adaptation in the Eastern Himalayas. These validated CS-IFS frameworks offer a scalable, decentralized approach to transition vulnerable smallholders from high-risk monoculture to resilient, entrepreneurial agrifood systems in hilly agroecosystems.

Keywords

1 Introduction

Global food security relies heavily on over 500 million smallholder farm families managing less than 2 hectares, representing approximately 88% of all operational land holdings worldwide (FAO, 2025; Mehrabi, 2023; Lowder et al., 2025). Agriculture faces the dual pressure of feeding a growing population while mitigating climate change, yet around 828 million people suffered from hunger in 2021 (FAO et al., 2022; Waqas et al., 2025; Sahoo et al., 2025; Wannasingha et al., 2025). In India, severe land fragmentation has reduced average holding sizes from 2.28 ha in 1970–1971 to 0.74 ha in 2021–2022 (GoI, 2024; Das et al., 2021; Bisht et al., 2020). To meet the food demands of an estimated 1.6 billion people by 2050, agricultural output must increase by 32% on a diminishing per-capita land base (Ngachan and Das, 2018; Ritchie et al., 2018; Smith, 2013). In the rugged agro-ecosystems of Northeast India and the Eastern Himalayas, where agriculture sustains over 70% of the rural population, small and marginal holders are constrained by high input costs, traditional land tenure, climate variability and limited economic opportunities (DoES, 2023; Makdoh et al., 2025). Smallholders face compounding vulnerabilities from climate variability, natural resource degradation and market unpredictability (Jose and Dollinger, 2019; Altieri et al., 2015). Small and marginal farmers relying on conventional agriculture face limited income and employment opportunities. Traditional fish farming is further constrained by costly feeds and fertilizers, costs that can be offset by incorporating livestock droppings into an integrated system (Vincke, 1988). Sustainable production therefore requires a shift away from external-input dependence toward integrated resource planning that utilizes family labor and local resources to adapt to climatic variation, market unpredictability and local food culture (Altieri et al., 2015; Amekawa et al., 2010; Poulton et al., 2010; Kremen et al., 2012; van der Ploeg, 2013; van Vliet et al., 2015; Gupta et al., 2025; Naseem et al., 2025). Within such systems, harvested water serves multiple purposes, including irrigation, livestock rearing, and domestic use, while unutilized pond embankments can be repurposed for vegetable cultivation, composting, or livestock housing, thereby maximizing resource use throughout the year (Tajuddin, 1980; Mohamad et al., 1990; Babu et al., 2019; Das et al., 2013a; Das et al., 2013b; Fatima et al., 2023; Palsaniya et al., 2023). Agrifood systems also account for nearly 30% of global anthropogenic GHG emissions (FAO, 2024), with emissions rising 25% across Asia even as they decline elsewhere—underscoring the urgency of climate-resilient alternatives (Babu et al., 2023; Waqas et al., 2025). To address these challenges, Integrated Farming Systems (IFS) emerge as an effective climate-smart strategy (Kumari et al., 2019; Behera and France, 2016). By synergizing crops, horticulture, livestock, and fisheries, IFS optimizes resource recycling, lowers external input dependency, enhances economic returns, and reduces greenhouse gas emissions compared to conventional monoculture (Radhamani et al., 2003; Kumar et al., 2018; Gupta et al., 2026; Rajkumar et al., 2025; Manjunath and Itnal, 2003a; Manjunath and Itnal, 2003b; Ravisankar et al., 2007; Ravisankar et al., 2010; Shyam et al., 2023). Furthermore, components like poultry-fish or livestock integration provide robust economic buffering and climate resilience while fostering rural entrepreneurship (Singh et al., 2018; Das et al., 2021; Ray et al., 2020). Despite these proven advantages, empirical documentation of location-specific IFS models remains vital for hill agro-ecosystems. Therefore, this study aims to assess and validate climate-smart IFS models concerning resource utilization, profitability, carbon dynamics, employment generation and sustainable livelihoods among small and marginal hill tribal farmers in the Eastern Himalayas of India.

2 Materials and methods

2.1 Study location

The on-farm evaluation and validation study on Integrated Farming Systems (IFS) was conducted during 2021–2024 across 20 farmers’ fields (replicated five times) in Liarkhla village (25°74’65.32”N, 92°07’44.98”E) (Panda and Mohapatra, 1989), situated in the Ri-Bhoi district of Meghalaya, India (elevation 950–1010 m above MSL). This site was selected because its mid-altitude topography, subtropical climate, and acid soil regime represent typical fragile Eastern Himalayan hill agro-ecosystems. It captures the region’s typical physiography, micro-climate, undulating terrain, and high seasonal rainfall patterns. Furthermore, the area is characterized by resource-constrained tribal smallholders who rely heavily on integrated traditional farming, making it an ideal real-world setting to test system productivity, carbon dynamics, and economic viability. It is bounded by Kamrup district to the north, Jaintia Hills and Karbi Anglong district of Assam to the east, and West Khasi Hills district to the west (Islam et al., 2024). The region experiences an average annual rainfall of 2,500 to 3,000 mm under sub-tropical hill agro-climatic conditions, with over 90% of rainfall occurring between June and September (Choudhury et al., 2012). July and August record the maximum rainfall. Despite heavy monsoonal rainfall, non-monsoon seasons (October–April) often face water deficits, reflecting the uneven seasonal distribution of rainfall. Maximum surface temperatures vary between 22 °C and 30 °C, with summer peaks of 36 °C (July–August) and winter lows reaching 2 °C (December–January) (Islam et al., 2024). The hilly terrain has acidic soils (pH 4.8) with an electrical conductivity of 0.27 dS m−1, soil organic carbon at 0.83%, medium available nitrogen (210 kg ha−1), low available phosphorus (8.9 kg ha−1), and medium available potassium (245 kg ha−1).

2.2 Integrated Farming System models

Three Integrated Farming System (IFS) models were assessed and validated on 0.5 ha each with five replications at 20 farmers’ field conditions:

  • Poultry (Vanaraja)–Fish–Vegetable–Vermicompost (PoultryFVV)
  • Duck (Khaki Campbell)–Fish–Vegetable–Vermicompost (DuckFVV)
  • Pig (Lumsniang)–Fish–Vegetable–Vermicompost (PigFVV)

These were compared with traditional single-enterprise fish farming (FF). Each IFS unit was stocked as per standard recommendations: 55 Vanaraja birds (PoultryFVV), 35 Khaki Campbell ducks (DuckFVV), and 3 Lumsniang pigs (PigFVV), alongside 800 Indian Major Carp (IMC) fingerlings stocked in a 0.1 ha pond. Supporting 0.35 ha of vegetable cultivation (broccoli, cabbage, tomato, potato, and French bean) and a vermicompost unit (10 × 3 × 2.5 ft) were included in each unit (ICAR, 2011).

Prior to selection, Khaki Campbell ducks were identified as superior for duckery through On-Farm Testing (OFT) comparing Indian Runner and Khaki Campbell breeds. The breeds were chosen based on (i) agro-climatic adaptability and disease resistance in the local region, (ii) production potential, feed-conversion efficiency, and synergy with on-farm crop residues (iii) market preference and local economic viability for smallholder farmers (ICAR, 2013).

2.3 Method of evaluation and validation

The IFS models were evaluated and validated for climate smartness through the assessment of carbon dynamics, climate resilience, profitability, employment generation, sustainability, and reduction in greenhouse gas emissions. The specific protocols are detailed below (Table 1).

Table 1. Summarizes the climate-smart agriculture (CSA) practices incorporated within the IFS models to address climate-related challenges.

SL.No.CSA practicesChallenges addressed
1Enterprise diversification❖ Enhanced production, stability, increased agro-biodiversity.❖ Incorporating multiple, stress-tolerant and short-duration crop varieties to diversify income streams, mitigating risks from crop failures.
2In-situ rainwater harvesting❖ Rainwater harvesting, water scarcity, enhanced production, GHG emission reduction, carbon sequestration.❖ Constructing farm ponds, trenches, and bunds to conserve water and increase groundwater recharge.❖ Using crop diversification and ridge-and-furrow methods to manage water stress
3Organic waste recycling/vermicompost❖ Enhanced production, stability, increased agro-biodiversity.❖ Incorporating multiple, stress-tolerant and short-duration crop varieties to diversify income streams, mitigating risks from crop failures.
4Boundary planting/Embankment utilization❖ Rainwater harvesting, water scarcity, enhanced production, GHG emission reduction, carbon sequestration.❖ Constructing farm ponds, trenches, and bunds to conserve water and increase groundwater recharge.❖ Using crop diversification and ridge-and-furrow methods to manage water stress
5Contingency cropping❖ Enhanced production, stability, increased agro-biodiversity.❖ Incorporating multiple, stress-tolerant and short-duration crop varieties to diversify income streams, mitigating risks from crop failures.

2.4 Carbon dynamics and GHG analysis

Carbon dynamics represent the environmental footprint of production (Babu et al., 2023). Carbon footprint (CF) indexing estimated greenhouse gas (GHG) emissions (methane - CH4, carbon dioxide - CO2, and nitrous oxide - N2O) from crops and livestock in carbon equivalent (CE) terms (Garg et al., 2016; Wei et al., 2024). Following Intergovernmental Panel on Climate Change (IPCC, 2006) guidelines, carbon emissions from enteric fermentation, manure and pond management, and farm operations were estimated using species-specific emission factors for poultry, ducks, and pigs to calculate annual carbon emissions (t CO2 eq. ha−1 yr−1).

Carbon sequestration was quantified based on changes in soil organic carbon (SOC) and biomass accumulation resulting from continuous vegetable cultivation, embankment vegetative cover, and vermicompost application. SOC was analyzed using the Walkley–Black method and converted to CO2 equivalents. Net carbon balance was calculated as the difference between carbon sequestration and emissions, where positive values indicate carbon sink behavior and climate mitigation potential (Bano et al., 2024).

GHG emissions from each IFS component were computed using the EXCEL model from ICAR-IIFSR (Subhash et al., 2018) based on activity data and their corresponding emission factors (EFs) Equation 1:

(1) GHG emission(Mg CO2 eq./yr)=A×Emission factor(EF)

Where A is the activity data and EF is the IPCC emission factor. Methane and nitrous oxide emissions were converted to CO2 equivalents using Global Warming Potentials (GWP) of 28 for CH4 and 298 for N2O.

2.5 Climate resilience indices

Based on the 30-year average kharif rainfall of 1,563 mm, year 2022 stands out as a ‘normal’ year, receiving 1,729 mm (+10.6%) with 137 rainy days, indicating well-distributed rainfall favorable for crops. In contrast, 2021 (1,313 mm, –16.0%, 80 rainy days) and 2023 (1,271 mm, –18.7%, 125 rainy days) clearly qualify as stress years marked by large deficits and uneven rainfall distribution. The year 2024 (1,445 mm, –7.5%, 102 rainy days, 13 heavy rain spells) represents a moderate stress condition, with rainfall closer to normal but heightened variability (Table 2).

Table 2. Rainfall patterns and climatic classification during the experimental period (2021–2024).

YearAnnual rainfall (mm)Deviation from normal (%)Rainy daysClimatic classification
20211313-16.0%80Stress Year
20221729+10.6%137Normal Year
20231271-18.7%125Stress Year
20241445-7.5%102Moderate Stress Year

The model-wise Climate Resilience Index (CRI) was evaluated through percentage yield stability during stress years compared to normal years Equation 2:

(2) CRI(%)=(Yield during stress year/Yield during normal year)×100

The Climate Resilience Adaptation Index (CRAI) was calculated by dividing the adaptive capacity by exposure plus sensitivity (Pandey and Jha, 2012) Equation 3:

(3) CRAI=Adaptive Capacity/(Exposure+Sensitivity)

Values range as follows:

  • 0.00 to 0.33 (Low resilience),
  • 0.34 to 0.66 (Medium resilience), and
  • 0.67 to 1.00 (High resilience).

Water quality parameters were analyzed following standard APHA protocols (APHA, 2012).

2.6 Profitability and sustainability metrics

Financial viability was assessed via Gross Cost, Gross Return, Net Return (NR), and Benefit-Cost Ratio (BCR) based on prevailing market rates (Sanjeev et al., 2012). Employment generation was quantified as man-days per year across components. Sustainability was evaluated using the Sustainable Value Index (SVI) and System Economic Efficiency (SEE) (Bohra and Kumar, 2015; Das et al., 2021; Bhat et al., 2023) Equation 4:

(4) SVI=(∑i=1nYi×Pi−∑j=1rXj×Rj)−∑(xi−x¯)2n−1Max(∑i=1nYi×Pi−∑j=1rXj×Rj)

Where, (∑i=1nYi×Pi−∑j=1rXj×Rj) is the net return from any IFS model, ∑(xi−x¯)2n−1 is the standard deviation of net returns across all IFS models, and Max(∑i=1nYi×Pi−∑j=1rXj×Rj) is the maximum net return achieved from any model. Where, x<sub>i</sub> is individual data, x¯ is mean data and ‘n’ is data points (herein, years).

The System Economic Efficiency (SEE) was calculated by dividing the net returns from an IFS model in a given year by 365 (Das et al., 2021) Equation 5.

(5) SEE=(∑i=1nYi×Pi−∑j=1rXj×Rj)365

Where, (∑i=1vYi×Pi−∑j=1rXj×Rj) is the net return from any IFS model and Yi is the ith enterprise’s output and Pi, prevailing price; and i = 1….n, total numbers of enterprises. Xj and Rj is the jth input and its price and j=1……r is the numbers of inputs used in production.

2.7 Statistical analysis

Data on mean production and economic performance were analyzed using one-way ANOVA classification/Randomized Complete Block Design (RCBD) using IBM SPSS statistics (Version 26.0). Standard error of mean (SEm±) was calculated for each parameter. The critical difference (CD) at a 5% probability level (p ≤ 0.05) was computed using Tukey’s Honestly Significant Difference (HSD). Other integrated parameters and system indices were evaluated via standard error of difference (SEd) and critical difference (CD at p ≤ 0.05) following the standard agricultural procedures outlined by Gomez and Gomez (1984) and Raudonius (2017) Equation 6.

(6) SEm±=s√n

Where ‘s’ is the standard deviation, and n = the number of years Equation 7.

(7) s=∑(xi−x¯)2n−1

Where ‘s’ is the standard deviation, x is individual data, x is mean data and ‘n’ is data points (herein, years).

The standard error of the difference (SEdiff) and critical difference (CD) were calculated as follows (Raudonius, 2017) Equation 8.

(8) SEdiff=2 ×s2n

Where, SEdiff is the standard error of the difference, s2 is the variance of the sample (square of the standard deviation) and ‘n’ is the observation numbers (herein, years) Equation 9.

(9) CD=t×SEdiff

Where ‘t’ is the ‘t’ value from statistical tables.

3 Results and discussion

3.1 Carbon dynamics and footprint analysis

The integrated farming system (IFS) models demonstrated an annual greenhouse gas emission of approximately 3,653 kg CO2-eq per 0.5 ha, equivalent to 7,306 kg CO2-eq ha−1 yr−1. Among the three IFS models, the PoultryFVV system exhibited the highest methane CO2-equivalent under high emission scenarios (1,536.8 kg CO2-eq), followed by DuckFVV (1,327.2 kg CO2-eq) and PigFVV (978.5 kg CO2-eq) systems (Tables 2, 3). This variation reflects differences in manure volume, nitrogen excretion rates, and manure management practices across species. The PigFVV system recorded higher CH4 emissions under low scenarios due to greater volatile solids output, while PoultryFVV systems exhibited higher emissions under high MCF conditions, indicating sensitivity to manure storage methods. Nitrous oxide emissions ranged from 137–150 kg CO2-eq annually and were primarily driven by nitrogen excretion and microbial transformation in manure, reflecting IPCC Tier-1 pathways for livestock systems.

Table 3. Greenhouse gas dynamics and component-wise emissions (kg CO2-eq. yr−1) across IFS models.

IFS componentsPoultryFVVDuckFVVPigFVV
Methane Emission (High Scenario)1536.81327.2978.5
Methane Emission (Low Scenario)210.3180.5295.4
Nitrous Oxide Emission137.4145.8150.2
Fishery Feed Production1800.01800.01800.0
Pond Methane Emission512.4512.4512.4
Vermicompost Mitigation effect-50%-50%-50%

The fishery component significantly contributed to total emissions, particularly through feed production (1,800 kg CO2-eq yr−1), reinforcing global findings that aquaculture feed is a dominant GHG hotspot in integrated systems. Pond methane emissions (512.4 kg CO2-eq yr−1) further amplified the system footprint, especially under warmer tropical conditions that enhance anaerobic decomposition. However, vermicomposting demonstrated mitigation potential by reducing methane emissions by approximately 50% from manure handled, confirming its role as an important climate-smart practice. These findings align with IPCC and FAO reports which emphasize that integrated systems with efficient waste recycling and improved manure management significantly lower net greenhouse gas emissions while sustaining productivity and livelihood resilience.

Under the Low Methane Conversion Factor (MCF) scenario, the total carbon footprint of the integrated farming systems ranged from 698.9 kg CO2-eq yr−1 in the DuckFVV model to 765.4 kg CO2-eq yr−1 in the PigFVV system (Table 4). Among the three models, the DuckFVV system consistently recorded the lowest emissions, indicating comparatively better environmental performance under reduced methane conversion assumptions. When the High MCF scenario was applied, total emissions increased substantially across all systems. The highest carbon footprint was observed in the PoultryFVV system (1,674.2 kg CO2-eq yr−1), while the DuckFVV system again showed the lowest emissions (1,473.0 kg CO2-eq yr−1). Carbon sequestration remained constant at 2.28 kg CO2-eq yr−1 across all systems, reflecting similar contributions from vermicomposting practices.

Table 4. Carbon footprint, sequestration, and net carbon balance (kg CO2-eq. yr−1) under varying MCF scenarios.

IFS modelsTotal carbon footprint (Low MCF)Total carbon footprint (High MCF)Carbon sequestrationNet carbon balance (High MCF)
PoultryFVV724.5b1674.2a2.281671.92a
DuckFVV698.9c1473.0c2.281470.72c
PigFVV765.4a1540.8b2.281538.52b
SEm (±)10.822.4—22.4
CD (p = 0.05)32.868.1—68.1

Treatment means within a column (or row) with different lowercase letters (a, b, c) differ significantly at p ≤ 0.05 based on Critical Difference (CD).

3.2 Climate resilience and yield stability

System productivity under normal conditions (2022) was the highest in the PoultryFVV system (6.00 t unit−1), followed by DuckFVV (5.90 t unit−1) and PigFVV (3.56 t unit−1). During stress years, productivity declined only marginally to 5.56, 5.43, and 3.28 t unit−1, resulting in high resilience indices of 93%, 92%, and 92% in PoultryFVV, DuckFVV, and PigFVV systems, respectively (Table 5) (Baruah et al., 2024; Sahoo and Behera, 2017). The PoultryFVV IFS system exhibited the highest overall resilience, with relatively low exposure (E = 0.400) and sensitivity (S = 0.500) coupled with the highest adaptive capacity (AC = 0.633), placing it in the high resilience category (CRAI > 0.67) (IPCC, 2014).

Table 5. Productivity resilience and Climate Resilience Adaptation Index (CRAI) of IFS models.

IFS modelNormal yield (t/unit)Stress yield (t/unit)Climate resilience index (%)CRAI valueResilience category
PoultryFVV6.00a5.56a93%0.703aHigh Resilience
DuckFVV5.90a5.43a92%0.455bMedium Resilience
PigFVV3.56b3.28b92%0.385cLow/Medium Resilience
SEm (±)0.120.111.20.015—
CD (p = 0.05)0.370.34NS0.046—

Treatment means within a column (or row) with different lowercase letters (a, b, c) differ significantly at p ≤ 0.05 based on Critical Difference (CD), NS indicates P > 0.05.

3.3 Economic evaluation and livelihood security

Evaluation of growth, reproductive, and economic traits across duck breeds indicated that the improved breeds significantly outperformed the indigenous ducks (Table 6). The Khaki Campbell breed exhibited the highest body weight, greatest annual egg production (260 eggs/year), and earliest sexual maturity (150 days), thereby minimizing the non-productive rearing period. Furthermore, its low feed conversion ratio (FCR = 2.5) reflected optimal nutrient utilization and reduced per-egg production costs, confirming its superior genetic potential compared to all other evaluated breeds.

Table 6. Productive traits and economic efficiency of different duck breeds under on-farm testing.

Duck breedMale weight (kg)Female weight (kg)Sexual maturity (days)Annual egg productionFCRNet income (US$)
Khaki Campbell2.54a2.30a150c260a2.5c33.73a
Indian Runner2.40a2.10b180b220b3.0b26.51b
Indigenous2.00b1.50c210a120c4.0a11.57c
SEm (±)0.060.054.25.80.080.78
CD (p = 0.05)0.180.1512.817.60.242.38

Treatment means within a column (or row) with different lowercase letters (a, b, c) differ significantly at p ≤ 0.05 based on Critical Difference (CD).

Among the evaluated models, the PoultryFVV system achieved the highest returns, generating a gross return (GR) of US$2,952.77, a net return (NR) of US$1,757.59, and a benefit–cost ratio (BCR) of 2.47 (Table 7), followed by the PigFVV and DuckFVV systems. The IFS models also generated substantial employment for rural households, with labor demand highest in the PoultryFVV model (227 man-days), followed by DuckFVV (204 man-days) and PigFVV (178 man-days). Correspondingly, the PoultryFVV model recorded the highest rural entrepreneurship success rate (67%), indicating enhanced livelihood security and income generation for smallholder and marginal tribal farmers in the Eastern Himalayas. In contrast, traditional monoculture fish farming (FF) yielded markedly low returns, with a net return of only US$65.12.

Table 7. Economic returns, employment generation, and entrepreneurial success rates across different farming models.

Farming modelGross return (US$)Net return (US$)BCREmployment (man-days/yr)Entrepreneur success (%)
PoultryFVV2952.77a1757.59a2.47a227a67%
DuckFVV2480.50c1320.40c2.14c204b60%
PigFVV2890.10b1650.30b2.33b178c48%
Traditional FF183.40d65.12d1.55d25dN/A
SEm (±)34.2022.500.034.2—
CD (p = 0.05)104.8068.900.0912.9—

Treatment means within a column (or row) with different lowercase letters (a, b, c) differ significantly at p ≤ 0.05 based on Critical Difference (CD).

3.4 Environmental and system sustainability

Water quality parameters remained well within the optimal ranges for aquaculture across all systems: pH ranged from 7.30 to 7.50, and dissolved oxygen remained safely above 5.0 mg/L and statistically significant (Boyd and Tucker, 2012; El-Sayed, 2006). Sustainability indexing revealed that the PoultryFVV system achieved the highest Sustainable Value Index (SVI = 0.561), whereas the PigFVV system achieved significantly the highest System Economic Efficiency on a daily basis (US$4.82/day) due to the high market value of pig biomass (Table 8).

Table 8. Water quality parameters (Mean ± SD) and system sustainability indices across evaluated IFS models.

IFS modelPond water pHDissolved oxygen (mg/L)Sustainable value index (SVI)System economic efficiency (SEE; US$/day)
PoultryFVV7.42 ± 0.09ab5.85 ± 0.15a0.561 ± 0.2194.20b
DuckFVV7.50 ± 0.10a6.00 ± 0.18a0.362 ± 0.2193.62c
PigFVV7.30 ± 0.08b5.40 ± 0.20b0.429 ± 0.2194.82a
SEm (±)0.050.100.1260.11
CD (p = 0.05)0.160.31NS0.34

Treatment means within a column (or row) with different lowercase letters (a, b, c) differ significantly at p ≤ 0.05 based on Critical Difference (CD), NS indicates P > 0.05.

4 Conclusions

The Poultry–Fish–Vegetable–Vermicompost (PoultryFVV) model significantly outperformed all evaluated IFS configurations, yielding the highest profitability, returns, and rural labor demand. All IFS models retained >92% baseline productivity during deficient rainfall, with the poultry-based configuration recording the highest Climate Resilience Adaptation Index (CRAI). Efficient nutrient recycling through vermicomposting and livestock integration preserved water quality and substantially lowered the overall carbon footprint. The initiative fostered 47 new agri-enterprises across 17 villages, achieving a 67% adoption success rate in poultry-based systems. Hence, Poultry-based IFS serves as a scalable, climate-smart, and commercially viable model for strengthening smallholder livelihoods and ecological stability in Eastern Himalayan hill agroecosystems.

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Footnotes

Edited by: Andrew Juan Challinor, University of Leeds, United Kingdom

Reviewed by: R Sammauria, Sri Karan Narendra Agriculture University, India

Esther Lalruatsangi, Central Agricultural University, India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The authors sincerely acknowledge the Indian Council of Agricultural Research, Department of Agriculture Research and Education, Government of India, including the ICAR-CRIDA, Hyderabad, ICAR-ATARI, Umiam, Meghalaya and ICAR Research Complex for NEH Region, Umiam, Meghalaya, India, for providing the funding, facilities, and other necessary support for conducting the study.

Data availability

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Author contributions

MB: Writing – review & editing, Formal analysis, Software, Writing – original draft, Data curation, Validation, Methodology, Investigation. MI: Project administration, Methodology, Supervision, Validation, Funding acquisition, Conceptualization, Writing – review & editing, Writing – original draft. PP: Data curation, Validation, Conceptualization, Methodology, Writing – review & editing. AM: Resources, Writing – review & editing, Visualization, Funding acquisition, Project administration, Supervision.

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Citation

Baruah, M. S., et al. (2026). Sustaining hill agriculture: climate-smart integrated farming systems for tribal smallholders in Eastern Himalayas, India. Frontiers in Agronomy, Volume 8. https://doi.org/
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Copyright © 2026 Baruah, Islam, Pathak and Mohanty.

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*Correspondence: Mokidul Islam

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