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Dynamics of co-operative credit and its impact on agricultural development in Kerala
(Department of Agricultural Economics, College of Agriculture, Vellayani, 2026-02-11) Divyapriya Rahul; Anil Kuruvila
The thesis titled “Dynamics of co-operative credit and its impact on agricultural
development in Kerala” examines the long-term development and role of co-operative
agricultural credit in Kerala and its impact on agricultural growth and farmers’
livelihoods during the period from 1980-81 to 2022-23. Co-operative institutions,
especially Primary Agricultural Credit Societies (PACS) and Primary Co-operative
Agricultural and Rural Development Banks (PCARDBs), have been important sources
of rural finance in Kerala. They provide loans to farmers for crop production, irrigation,
machinery, plantation development, and other agricultural activities. Despite the
importance, the distribution and effectiveness of co-operative credit have differed
widely across districts and over time. This study aims to understand how co-operative
credit has evolved, how it has contributed to agricultural development, and how it has
affected individual farming households in Kerala.
The study is based on both secondary and primary data. Secondary data were
collected from government reports, institutional records, and official databases relating
to co-operative credit and agricultural development. These data cover all districts of
Kerala and span more than four decades. Primary data were collected from 160 farmer
borrowers selected from four districts, viz., Pathanamthitta, Alappuzha, Malappuram,
and Palakkad. These districts were chosen to represent areas with low and high levels
of credit disbursement and different agricultural systems. This combination of data
sources enabled a comprehensive analysis of both long-term trends and household-level
impacts. The analysis was guided by three main objectives. The first objective was to
examine the changing pattern and growth of co-operative credit in Kerala. The second
objective was to assess the contribution of co-operative credit to agricultural
development, while the third objective was to study the impact of co-operative credit
on farmers’ income and standard of living. Various statistical and econometric tools
such as growth rate analysis, inequality measures, cointegration tests, causality analysis,
and regression models were used to achieve these objectives.
ii
To study the growth and performance of PACS and PCARDBs, the entire study
period was divided into three phases, which were identified on the basis of structural
break analysis. The first phase (1980-81 to 2001-02) was a period of rapid expansion
and institutional strengthening. During this period, most districts experienced strong
growth in membership, deposits, share capital, working capital, and credit
disbursement. Farmers increasingly trusted co-operative institutions and deposited their
savings in them. Deposit mobilisation became the main source of funds, which reduced
excessive dependence on external borrowing. Both short-term crop loans and long-term
investment loans expanded, indicating rising agricultural activity and investment.
However, some districts such as Kottayam and Thrissur showed relatively weaker
growth, suggesting early signs of saturation or limited investment demand. The second
phase (2002-03 to 2011-12) was a period of transition. During this period, the growth
of co-operative institutions slowed down in many districts. Competition from
commercial banks, the spread of Kisan Credit Card scheme and changes in rural finance
policies affected the functioning of PACS. Deposit mobilisation continued, but growth
in share capital weakened in several regions. As a result, co-operatives became more
dependent on borrowings from higher financing agencies. While some districts such as
Palakkad, Kozhikode, and Malappuram performed relatively well, others like Wayanad,
Kottayam, and Thiruvananthapuram recorded stagnation. Credit expansion also became
concentrated among fewer borrowers in some areas, reducing inclusiveness. The third
phase (2012-13 to 2022-23) represents a period of partial revival and institutional
stabilisation. Membership and share capital increased in many districts, reflecting
renewed confidence among farmers. Internal resource mobilisation improved, and
dependence on external borrowings declined compared to the previous phase. Long
term investment credit expanded in several districts, especially for plantation
development, irrigation, and mechanisation. However, short-term and medium-term
credit growth slowed in many regions due to increased competition from banks and
digital credit platforms. Performance across districts remained uneven, with some
districts showing balanced growth while others lagged behind.
A similar analysis was carried out for PCARDBs, which mainly provide long
term agricultural loans. In the first phase, several districts such as Wayanad, Palakkad,
iii
and Ernakulam recorded strong institutional and credit growth. However, dependence
on external borrowings was high in many areas. In the second phase, institutional
restructuring improved financial stability in some districts, but growth remained
selective. In the third phase, long-term credit expanded sharply, indicating renewed
investment activity, but this expansion was often supported by external funds rather than
internal resources and as a result, concerns about the financial autonomy persisted. The
study also analysed the regional distribution of medium-term and long-term co
operative credit. The results showed that credit distribution in Kerala has been highly
unequal across districts throughout the study period. A few districts such as Ernakulam,
Kottayam, Thrissur, and Kollam consistently received a large share of investment credit,
while districts such as Idukki, Wayanad, Kasaragod, and Alappuzha remained under
served. Even after adjusting credit amounts based on cultivated area, inequalities
remained high. This indicates that institutional strength, cropping patterns, and
historical development play more important roles than land size in determining the
credit access. Established co-operative networks and diversified commercial agriculture
enabled certain districts to attract more credit, while weaker regions faced persistent
constraints.
To assess the contribution of co-operative credit to agricultural development, the
study examined the long-term and short-term relationships between credit and
agricultural indicators. The results show that co-operative credit has a stable
relationship with cropping intensity, fertilizer use, irrigated area, plantation area, and
agricultural output. This means that credit and agricultural development have moved
together over time. In the short run, credit availability was found to encourage better
farming practices and investment in agriculture. However, the impact of credit on output
and income was gradual rather than immediate.
The micro-level analysis based on primary data provides important insights into
how farmers use co-operative credit. The sample farmers were mostly older,
experienced cultivators with low levels of formal education and small landholdings.
This reflects the ageing nature of agriculture in Kerala and limited participation of
younger generations. Most households belonged to the medium-income category, with
income levels influenced by crop diversification and non-farm activities.The study
iv
found that the amount of credit sanctioned to farmers was mainly determined by
collateral availability and existing indebtedness rather than by actual production needs.
Factors such as land size, education, and cultivation expenses had little influence on
loan size. This indicates that lending decisions were guided more by institutional rules
and risk considerations than by farm requirements. As a result, credit allocation was not
always aligned with the productive potential. The study also found that farm income
was influenced mainly by cultivated area, production expenditure, and farming
experience. The amount of co-operative credit borrowed did not have a significant direct
impact on income. This suggests that credit alone does not guarantee higher income
unless it is used effectively for productive investments. The analysis of loan utilisation
revealed that a considerable number of borrowers diverted loans for non-productive
purposes such as household consumption, repayment of old debts, medical expenses,
and coping with climatic or wildlife-related losses.. Consequently, co-operative credit
often functioned as a means of stabilising household consumption and managing risks
rather than promoting productive investment.
The study concluded that Kerala’s co-operative credit system has expanded
significantly over the past four decades and has played a vital role in supporting
agricultural activities. However, its growth has been spatially uneven and institutionally
driven. Investment-oriented credit was more unequally distributed than short-term crop
loans, limiting development in weaker regions. At the macro level, co-operative credit
supports agricultural intensification and structural transformation. At the micro level,
its developmental impact is limited by risk-averse lending practices, ecological
challenges, and diversion of funds. The study recommends increasing the share of long
term investment credit, strengthening deposit mobilisation and share capital, adopting
district-specific revival strategies, aligning credit with agro-ecological conditions, and
linking credit with technology adoption and advisory services. It also emphasises the
need for flexible lending products, better monitoring of loan utilisation, and incentive
based repayment systems. Integrating credit with farm support services can improve
productivity and repayment capacity. These measures can enhance the effectiveness of
co-operative credit as a tool for sustainable and inclusive agricultural development in
Kerala.
Disease management for dry direct seeded rice (DDSR) under precision farming system
(Department of Plant Pathology, College of Agriculture , Vellanikkara, 2026-02-13) Mannadiar Abhinav Vijayan; Raji, P
Conventional rice cultivation by transplanting in puddled field requires large
amount of water and labour. Water for rice cultivation is becoming scarce due to
lowering of groundwater levels and also due to climate change. In Asia, rapid economic
development and industrialisation have led to shortage of labour in agriculture. Dry
direct seeded rice (DDSR) is a promising resource-saving alternative to transplanting
in puddled field and is gaining importance worldwide. The yield in general is 15 per
cent less in DDSR than in transplanting. By adopting appropriate crop management
such as precision farming integrated with modified planting geometry and suitable crop
protection strategies, yield maximisation is possible in DDSR. In this background, a
study was conducted to evaluate different disease management strategies for DDSR
under precision farming.
Field experiments were conducted at the Regional Agricultural Research
Station, Pattambi, during late rabi and summer seasons of the year 2025. The
experiments were laid out in Randomised Block Design with three replications using
rice variety Harsha. Dry direct sowing by dibbling at a spacing of 35-15 cm × 10 cm in
paired row planting geometry was followed. The treatments included various
combinations of biocontrol agents viz., Arbuscular Mycorrhizal Fungi (AMF),
Pseudomonas fluorescens and Trichoderma asperellum. AMF was applied to soil at the
time of sowing, Pseudomonas fluorescens and Trichoderma asperellum were applied
as seed treatment and through drip. These were compared with normal recommendation
of P. fluorescens and T. asperellum as seed treatment, soil application and foliar spray
along with a fungicide/antibiotic check and an untreated control.
Diseases observed during both late rabi and summer season experiments were
sheath blight, bacterial blight and brown spot. Disease incidence and severity were
recorded at weekly intervals. The results revealed that the treatment T6 - soil application
of AMF (2.5 kg/ha) + P. fluorescens - seed treatment (10 g/kg seed) and two applications
of through drip (2.5 kg/ha) + foliar spray of cowdung slurry (20 g/l) was effective for
the management of sheath blight, brown spot and bacterial blight. T5 - soil application
of AMF (2.5 kg/ha) + T. asperellum - seed treatment (10 g/kg seed) and two applications
through drip (2.5 kg/ha) was also equally effective as T6 for the management of sheath
blight and brown spot.
The total rhizospheric population of fungi and bacteria were higher in treatments
where the biocontrol agents were applied. The populations of P. fluorescens and T.
asperellum were also higher in treatments in which the respective biocontrol agents
were applied. The root colonisation of AMF was higher in AMF applied treatments.
The biometric parameters viz., total number of tillers and number of leaves were highest
in T6 which were statistically on par with T5. The yield attributes viz., number of
productive tillers, number of grains per panicle and 1000 grain weight were
significantly high in T6 and were statistically on par with T5. The grain yield recorded
was also highest in T6 (7343 kg/ha) which was statistically on par with T5 (7211 kg/ha).
The overall results show that soil application of AMF (2.5 kg/ha) +
Pseudomonas fluorescens - seed treatment (10 g/kg seed) and two applications through
drip (2.5 kg/ha) at tillering and panicle initiation stages + foliar spray of cowdung slurry
(20 g/l) at the time of booting is effective for the management of bacterial blight, sheath
blight and brown spot of rice in DDSR under precision farming with drip irrigation and
fertigation. This study implies that alterations in planting geometry along with
integration of biological control agents in DDSR under precision farming can have a
significant influence not only in disease management but also in yield enhancement.
Economic analysis of banana cultivation in Thiruvananthapuram district
(Department of Agricultural Economics, College of Agriculture , Vellayani, 2026-01-29) Karthika Pillai, P; Thasnimole, F
Banana (Musa spp.) is a major global fruit crop, widely cultivated for its
adaptability and nutritional richness. It plays a crucial role in ensuring food and
nutritional security in many developing countries. In Kerala, banana production reached
4,11,876.74 tonnes in 2023-24, with Thiruvananthapuram district alone contributing to
17.46 per cent of production from an area of 7,814.15 ha, and recording an average
productivity of 9.20 tonnes per ha. Among the major varieties cultivated in the region,
Red banana (locally known as Chenkadali) and Njalipoovan hold particular cultural and
economic importance in southern Kerala. In this context, the present study entitled
“Economic analysis of banana cultivation in Thiruvananthapuram district” was
undertaken to evaluate the economics of cultivation of these two varieties, examine their
marketing systems, estimate pre- and post-harvest losses, analyse the determinants of
crop insurance adoption, and identify the major constraints faced by banana farmers.
The study was carried out in four panchayats of Thiruvananthapuram district-
Kalliyoor, Maranalloor, Kunnathukal, and Kollayil. Primary data were collected from
160 farmers (80 farmers cultivating each variety) and 50 marketing intermediaries using
a well-structured and pre-tested interview schedule. Data pertaining to the agricultural
year 2024-25 were used for the analysis. The socioeconomic characteristics revealed
that most of the farmers belonged to the age group of 56-65 years and depended on
agriculture as their major occupation. The majority operated on marginal landholdings
of less than one hectare, with more than 20 years of farming experience, and education
upto SSLC level. Njalipoovan growers mainly cultivated on owned land (63.75%),
whereas 42.5 per cent of Red banana farmers relied on leased in land for cultivation.
The cost of cultivation was analysed using CACP cost concepts. The total cost
of cultivation of Red banana was ₹6,80,520 per ha, with operational costs accounting
for 77.25 per cent. Labour and material inputs contributed 55.20 per cent and 39.13 per
cent of operational costs, respectively. For Njalipoovan, the total cost of cultivation was
₹5,64,334 per hectare, of which 74.19 per cent comprised operational costs. Within
operational costs, labour (57.26%) and material inputs (39.31%) constituted the highest
i
share. The estimated BC ratio was 1.61 for Red banana and 1.87 for Njalipoovan,
indicating that both varieties were profitable in the study area.
Resource use efficiency was analysed using the Cobb-Douglas production
function. The results indicated that while suckers were underutilised in both the
varieties, human labour was overutilised in Red banana cultivation. Weather conditions
exerted a negative and significant influence on returns in both the crops. Moreover,
plant protection chemicals negatively affected the yield of Red banana. Five marketing
channels were identified, among which Channel V (Producer → Retailer → Consumer)
recorded the highest producer’s share in consumer’s rupee with 83.69 per cent for Red
banana and 82.83 per cent for Njalipoovan. Also, this channel was found to be the most
efficient.
Pre-harvest loss assessment revealed that weather related factors were the
principal cause of damage, accounting for 15.13 per cent in Red banana and 26.18 per
cent in Njalipoovan. Total pre-harvest losses were 21.90 per cent and 33.63 per cent,
respectively for Red banana and Njalipoovan. Post-harvest losses across the marketing
chain were found to be 18.94 per cent for Red banana and 18.03 per cent for
Njalipoovan, with retail-level losses being the highest at 15.26 and 12.59 per cent
respectively for these varieties. Bootstrapped confidence intervals provided statistical
robustness to these estimates. It was observed that only 35.63 per cent of the farmers
had availed insurance in the study area. Binary logistic regression was used to identify
the factors influencing adoption of crop insurance by banana farmers. Farm income,
previous insurance experience, and land ownership were found to positively influence
the decision to adopt crop insurance. Lack of trust in timely compensation, absence of
land revenue receipts, and complicated procedures were identified as the major reasons
for non-adoption.
The constraints faced by banana farmers were analysed using Garrett’s ranking
technique. The result revealed that unfavourable weather condition was the most serious
challenge affecting both the varieties. This was followed by the rising cost of inputs and
fluctuations in market prices. While Red banana growers identified price volatility as
the third major constraint, Njalipoovan farmers ranked pest and disease incidence as the
third most important constraint. Labour scarcity and the high cost of labour were also
ii
reported as a major constraint that adversely affected the banana production process.
The study suggests that ensuring the availability of quality planting materials,
promoting scientific cultivation practices recommended by Kerala Agricultural
University, enhancing labour availability, strengthening organised procurement
systems, establishing mechanisms to stabilise prices, expanding digital marketing
platforms and simplifying crop insurance procedures will be essential to improve
productivity, reduce risk, and enhance the overall profitability of banana cultivation in
Thiruvananthapuram district.
Technology adoption and risk management by young vegetable farmers in southern Kerala
(Depaertment of Agricultural Extension Education, College of Agriculture, Vellayani, 2026-02-03) Amritha, K S; Sreedaya, G S
The present study entitled "Technology adoption and risk management by young
vegetable farmers in Southern Kerala" was conducted with the objectives of assessing
KAU technology adoption, analyzing impact on production and income levels, determining
factors influencing adoption, and analyzing risk perception and management strategies. The
study was conducted in Thiruvananthapuram and Alappuzha districts, purposively selected
for having highest area under vegetable cultivation and highest urban populations in
Southern Kerala. Seven blocks were selected from each district with five commercial
farmers randomly selected from each block, and seven wards were selected from
Thiruvananthapuram Corporation and Alappuzha Municipality with five urban farmers
selected from each ward. The sample included 140 participants: 70 commercial farmers (35
from each district) aged 25-45 years with minimum landholding of 1.5 acres and maximum
respondents cultivating all of the selected crops (bhindi, tomato, brinjal, chilli, and
cowpea), and 70 urban farmers (35 from each district) aged 25-45 years engaged in
rooftop/house terrace cultivation with minimum 30 growbags or containers.
The study's dependent variable was extent of KAU technology adoption assessed
using adoption index. Other variables that were assessed include impact of the adopted
technologies, was evaluated using MAPP technology, factors influencing adoption were
analyzed using TAM, risk perception was measured using risk perception index and risk
management index was analysed using mean, with ten independent variables selected to
understand respondents profile characteristics.The statistical tools including frequency and
percentage analysis, mean, standard deviation, t-test, Kruskal-Wallis test with Dunn's post
hoc test, multiple regression, Spearman's correlation, and skewness and kurtosis analysis.
The study found that majority of 57.14% fell in the medium-high age category with a
mean age of 40.13 years (SD 5.40). Urban farmers demonstrated higher educational
qualifications, with 68.57% being college educated. Overall, 51.43% were male and
48.57% female, with urban farming showing higher female participation. The mean
cultivation area was 834.29 square feet (SD 383.09) for urban farmers and 3.80 acres (SD
3.72) for commercial farmers. Extension orientation was significantly higher among
commercial farmers (mean 8.68, SD 4.49). Majority demonstrated medium innovativeness
212
and credit orientation. Agriculture was the primary occupation for 80% of commercial
farmers, while it was secondary for 81.43% of urban farmers. Mean annual income was
3.72 lakhs (SD 2.72) for commercial farmers, with majority in the low-medium category in
both districts (48.57% and 50% in Alappuzha and Thiruvananthapuram, respectively), and
6.00 lakhs (SD 5.27) for urban farmers, with a majority of 70.58% in the low-medium
category in Alappuzha district and 48.57% in the medium-high category in
Thiruvananthapuram district. Mean cultivation experience was 14.61 years (SD 7.72), with
majority in the medium-high category in both districts for commercial farmers (42.86%
and 37.14% in Thiruvananthapuram and Alappuzha, respectively), and 9.49 years (SD
6.53), with majority of 42.86% in the low-medium category in both districts for urban
farmers.The study showed that mean adoption index for commercial farmers in Alappuzha
was 44.56 with standard deviation of 8.34 with majority of 37.10 % in medium-low
adoption category, while in Thiruvananthapuram it was 44.14 with standard deviation of
8.50 with 34.29 % each in medium-low and medium-high categories, with no significant
difference between districts, while mean adoption index for urban farmers in Alappuzha
was 29.93 with standard deviation of 14.87 with majority of 31.43 % in medium-low
category, and in Thiruvananthapuram it was 36.79 with standard deviation of 18.87 with
majority of 34.29 % in medium-low category.
The study revealed that impact assessment showed substantial increase in vegetable
production in both districts, nutrient management, land preparation, and plant protection
were most valued interventions with district-specific variations in irrigation, weed
management, and varieties, yield of major vegetables and average farmer income were
most influenced indicators with plant protection and land preparation showing highest
influence, trend analysis revealed positive trends in yield and farmer income despite
differences in crop diversity, climate-related losses and return on investment, and impact
profile indicated positive impacts for production and income with challenges in addressing
climate change and maintaining crop diversity.
The study showed that factors influencing adoption among commercial farmers of
Alappuzha included PU (0.392), PEOU (0.334), and IU (0.426) were significant, while in
Thiruvananthapuram, PU (0.337) and IU (0.348) were significant, among urban farmers,
IU was strongest in Alappuzha (0.536), along with significant PU (0.334) and PEOU
213
(0.353), while PU (0.345) and IU (0.383) were significant in Thiruvananthapuram, and
comparison revealed significant differences in technology and economic factors, with
commercial farmers having higher PU and PEOU, and no significant differences in
institutional and environmental factors for PU and PEOU respectively.
The study found that risk perception among commercial farmers showed Market Risk
was most severe in Alappuzha (55.66) while Production Risk was most critical in
Thiruvananthapuram (58.71), urban farmers in Alappuzha showed Institutional Risk as
most severe (45.71) while Production Risk was primary concern in Thiruvananthapuram
(49.90), comparison revealed highly significant differences in Market Risk (t-value 6.32 in
Alappuzha, 8.16 in Thiruvananthapuram) and Financial Risk (t-value 2.91 in Alappuzha,
8.04 in Thiruvananthapuram), and specific risk sources ranking highest included climatic
variations and pest incidence for production risk, overproduction, price fluctuations,
perishability and low price for market risk, government regulations, credit policy changes,
insufficient support and limited extension linkage for institutional risk, and inadequate
credit availability and high interest rates for financial risk. Risk management strategies
revealed planting high-yielding disease-resistant varieties and intensive methods ranked
highest for production risk, proper post-harvest management and market information for
market risk, participation in farmers' groups for personal risk, information on schemes and
extension contact for institutional risk, and improving farm documentation and income
diversification for financial risk, while focus group discussions (n=62) highlighted
reducing vegetable imports (77.4%), improving input quality (69.3%), and establishing
standardised produce collection (59.3%).
Regression results indicated among commercial farmers of Alappuzha, age and
gender (<0.01) and area of cultivation (<0.05), among urban farmers of Alappuzha,
innovativeness (<0.05), for commercial farmers of Thiruvananthapuram, extension
orientation and credit orientation (<0.01), age (<0.05), and innovativeness (p<0.1), while
among urban farmers of Thiruvananthapuram, innovativeness (<0.05), and extension
orientation and experience (p<0.1) shown as significant.
Nutri -sensitive agriculture in home gardens of Kerala : a multi -dimensional analysis
(Department of Agricultural Extension Education , College of Agriculture ,Vellayani, 2026-01-29) Soorya, P V; Chinchu, V S
Nutri-sensitive agriculture (NSA) is an integrated, food-based approach that
connects agricultural production with improved nutrition outcomes to achieve food and
nutritional security. Facing the challenges of hidden hunger and dietary imbalance, NSA
emphasizes the diversification of crops and livestock to make nutrient-rich foods more
available, accessible, and effectively utilized within households. Within this context,
the present study titled ‘Nutri-Sensitive Agriculture in Home Gardens of Kerala: A
Multi-Dimensional Analysis’ was undertaken to assess household awareness of nutri
sensitive agriculture, evaluate dietary diversity across regions, explore gender roles in
production and household decision-making, identify major barriers to its adoption, and
propose policies to promote and sustain NSA practices in the state.
The investigation was conducted in three districts of Kerala: Kasaragod, Thrissur,
and Thiruvananthapuram, representing the northern, central, and southern zones of the
state. A total of 180 respondents, with 60 from each district, were selected through a
multistage random sampling method. The study adopted an ex-post facto and
exploratory research design and employed both descriptive and inferential statistical
techniques. Correlation analysis, the Berry index for dietary diversity, Proportionate
distribution index for crop diversity, Garrett ranking for identifying constraints, and
Chi-square test for assessing gender participation were used. Independent variables
included age, gender, education, occupation, income, family size, landholding, and
social participation, while awareness, dietary diversity, gender participation, and
constraints served as dependent variables.
The findings revealed a generally high level of awareness among respondents
regarding nutri-sensitive agriculture. Nearly half of the respondents demonstrated
medium to high awareness of the nutritional benefits of NSA, showing a clear
understanding of how diversified home gardens enhance family nutrition through
vegetables, fruits, pulses, and livestock products. Awareness related to family well
being through nutrition-sensitive diets was also high, reflecting the recognition that
balanced, home-grown foods improve health and immunity. However, awareness of
government schemes and institutional support for NSA was relatively low, suggesting
that policy outreach and communication remain inadequate.
126
Dietary diversity assessed using the Berry index showed consistently high values
across all three regions. Kasaragod recorded an index value of 0.895, Thrissur 0.893,
and Thiruvananthapuram 0.889. These results indicate a well-balanced consumption
pattern across cereals, pulses, vegetables, fruits, and animal products. The small
variation among the regions highlights the uniform structure of Kerala’s multi-tier home
gardens, which combine plant and animal components. The high diversity values
demonstrate that home gardens are inherently nutrition-oriented, capable of maintaining
dietary adequacy and protecting households from food price fluctuations. The
distribution of crops revealed that vegetables were cultivated by almost all households,
followed by tubers and fruits, confirming the dominance of nutrient-dense crops in
home garden systems.
Gender analysis showed that women play a central role in nutri-sensitive
agriculture, particularly in household nutrition and food management. Women were
primarily responsible for meal planning, child feeding and monitoring, and ensuring
balanced diets, while men were more involved in land preparation, marketing, and
resource management. Chi-square analysis indicated significant gender differences
across major nutri-sensitive agriculture domains, including crop and livestock
management, decision-making, child nutrition, and household consumption patterns.
Overall, the findings highlight women as the key drivers of nutrition management
within households, complementing men’s roles in production and marketing.
The Garrett ranking analysis revealed multiple constraints affecting the adoption
of NSA. Operational constraints ranked highest (mean = 53.89), mainly due to climatic
variability (58.82) and wild animal attacks (57.56), followed by limited landholding
(54.94) and inadequate irrigation (42.50). Technological constraints (mean = 50.64)
were associated with poor knowledge of pest and disease management (56.49) and low
awareness of nutritional security (49.62). Economic constraints (mean = 48.63)
stemmed from high input costs (53.42) and weak financial conditions (50.21). Socio
personal constraints (mean = 46.13) included lack of family support (47.52) and low
literacy levels (44.64). The most critical constraint was inadequate financial support for
smallholders (58.94) under the policy and institutional domain (mean = 51.19), along
with poor policy integration (50.21) and limited publicity (46.46). Overall,
127
environmental, financial, and institutional constraints were the major barriers to nutri
sensitive agriculture adoption, indicating the need for stronger policy support, improved
credit access, and targeted technical training.
Based on the findings, the study recommends several strategic policy measures to
strengthen NSA in Kerala. Priority actions include enhancing policy and financial
support for smallholder farmers, establishing local seed banks for diverse and
underutilized crops, and providing subsidies for nutrient rich and climate resilient crop
varieties. The study also emphasizes the promotion of organic and sustainable farming
practices, strengthening research on nutritious crops and soil health, and improving
agricultural extension services to better integrate nutrition focused practices. In
addition, government supported interventions to reduce crop losses from wild animal
attacks, such as fencing subsidies, community based protection systems, and
compensation schemes, are essential. Developing value chains for nutrient dense foods,
expanding community based nutrition education, adopting gender responsive
agricultural policies, and strengthening coordination among the agriculture, health, and
nutrition sectors are crucial for the long term sustainability of NSA.
Kerala’s home gardens emerge as robust models of nutrition-sensitive farming,
effectively combining ecological diversity, women’s empowerment, and community
participation. Strengthening extension networks, enhancing nutrition education,
fostering inter-departmental collaboration, and creating policy-driven market linkages
for nutrient-dense crops are key to mainstreaming nutri-sensitive agriculture.
Embedding nutrition objectives within agricultural planning can transform Kerala’s
traditional home garden systems into dynamic engines of sustainable nutrition,
livelihood enhancement, and climate resilience-contributing significantly to broader
goals of health, equity, and environmental sustainability.