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ThesisItem Open Access Modelling climate change impact on surface runoff and sediment yield in a watershed of Shivalik region(Academy of Climate Change Education and Research, Vellanikkara, 2020) Anu Raj, D; KAU; Mary Regina, FThe climate change refers to the seasonal changes over a long duration in relation to the increasing amount of greenhouse gasses in the atmosphere. Global warming leads to a more vigorous hydrological cycle, including higher amount rainfall and more frequent high-intensity rainfall events. The Himalayan region is suffering from a serious problem of soil erosion and rivers flowing through this region transport a massive load of sediment. Climate change has a significant contribution to soil erosion. It leads to loss of nutrient-rich top soil which in turn can affect the nation’s food security. The present study depicts modelling climate change impact on surface runoff and sediment yield in a watershed of Shivalik region of Himachal Pradesh using a process-based Agricultural Policy/ Environmental eXtender(APEX) model. Terrain characteristics were analysed with the aid of Cartosat DEM. Land use/land cover characteristics were extracted from Resourcesat-2 LISS-IV and ground observations. Soil samples were collected from the field were analysed to identify soil physical and chemical properties. Surface runoff and sediment yield data required for model calibration and validation were collected from the gauging station constructed in the field. The future climate scenarios (temperature and rainfall) namely A2 and B2 of the study area were downscaled using statistical downscaling model (SDSM). APEX model parameterization was done as per local conditions. The APEX model was calibrated on a daily basis for 2017 and 2018. For calibration and validation of the model used low to medium rainfall days. The model calibrated quite well for surface runoff (r2 - 0.92) and sediment yield (r2 - 0.88) with RMSE of 4.98 mm and 0.20 t/ ha for surface runoff and sediment yield, respectively. The model was validated well for surface runoff (r2 - 0.81) and sediment yield (r2 - 0.81) with RMSE of 2.6 mm and 0.11 t/ha for surface runoff and sediment yield respectively. The model performance was identified based on Nash- Sutcliffe efficiency (NSE). The model performed quite well for surface runoff and sediment 224 yield of NSE 0.71 and 0.70 respectively. The change in soil loss under A2 and B2 scenarios with respect to baseline period were predicted for the study area to recognize the effect of climate change on soil loss. The general trend in future climate shows there is an increase in rainfall under both A2 and B2 scenario. Under the A2 scenario, rainfall increases marginally higher than B2 scenario. A total of 41.35 per cent increase in rainfall during 2080, 20.14 per cent during 2050, and 27.27 per cent during 2020 were observed. But in B2 scenario due to lower emission, change in rainfall is relatively lower than A2 scenario. It was observed that 24.71 per cent, 29.13 per cent and 35.16 per cent increase during 2020, 2050 and 2080 respectively. Maximum temperature increases 3.7 oC during 2080 under A2, while under B2 scenario the increase is 2.6 oC. Similarly, minimum temperature also rising at 3.6 oC during 2080 under A2 scenario and 2.7 oC under B2 scenario. The increase in temperature under both scenarios is almost similar and a marginal difference was observed. Highest soil loss was estimated from scrub land (38.42 t/ha/yr) followed by agriculture (26.97 t/ha/yr) then open forest (21.69 t/ha/yr) and lowest in the dense forest cover (14.70 t/ha/yr) under baseline period. The average annual soil loss from the watershed is 25.45 t/ha/yr. It was observed that 64.61 per cent of the study area was under moderate (10-20 t/ha/yr) erosion risk class. 24.15 per cent area with severe (20-40 ton ha-1 yr-1) erosion and 11.23 per cent area contribute very severe (>40 ton ha-1 yr-1) erosion. Under A2 scenario the average soil loss during 2020s, 2050s and 2080s may increase 27.71, 21.84 and 46.94 per cent respectively. Similarly under B2 scenario average soil loss may increase 23.24, 30.71 and 38.80 per cent, respectively. The climate change impact on soil erosion under both scenarios suggests that there is an increasing soil erosion due to the increase in rainfall in Shivalik region of Himachal Pradesh. Due to the high intensity of rainfall and steep slopes of the study area the mechanical conservation measures are preferred. The agronomic, mechanical and biological measures can be also used to conserve the soil and water.ThesisItem Open Access Structural transformation and spatio temporal variations of agriculture in Kerala(Department of Agricultural Economics, College of Horticulture, Vellanikkara, 2020) Otieno Felix, Owino; KAU; Anil, KuruvilaKerala had been undergoing several transformations in its agricultural sector. This was caused by the shift of resources like labourers from the agricultural sector to the secondary and tertiary sector. The transformation could also be seen where ever the rising agricultural wages affected the profitability of major crops due to the increase in the cost of production. The shortage of farm labourers coupled with other factors like uneconomic size of land holdings, sustained conversion of agricultural lands to non-agricultural uses and low profitability in agriculture had hampered the growth in the sector. The structural adjustments could also be observed in the state income in which there was an increase in share of income from the tertiary sector and decline in share from the primary sector. The decline in the share of agriculture in the state income was due to an aggregation of factors. Thus, the present study sought to find out the reasons leading to the transformations in Kerala agriculture by analysing the growth of agriculture and assessing the disparities among the districts in agricultural development in Kerala, examining the dynamics in land use and cropping patterns, studying the dynamics in economics, efficiency and profitability of cultivation of major crops and estimating the total factor productivity and its determinants for major crops in Kerala. The time series data for the period from 1970-71 to 2018-19 was used to understand the objectives of this study. The entire series was subjected to (Bai and Perron, 1998) methodology and six phases of growth were obtained, on which the entire study was based. These periods were Period I (1970-71 to 1980-81), Period II (1981-82 to 1987-88), Period III (1988-89 to 1994-95), Period IV (1995-96 to 2003-04), Period V (2004-05 to 2010-11) and Period VI (2011-12 to 2018-19). The Compound annual growth rates and Cuddy-Della Instability Indices were used to understand the growth performance of the crops. The results of the analyses of growth in crops revealed that food grains, tapioca, ginger and cashew had the largest loss in area throughout the period under study. Pulses, paddy, tapioca, cashew and ginger exhibited annual declines of -6.58 per cent, -3.89 per cent, -3.71 per cent, -2.44 per cent and -2.12 per cent in their area. These crops also had the lowest growth in productivities which affected the production. This was found to be due to fall in the prices of these crops, rising cost of inputs and rising prices of other crops. Rubber had the largest annual rise in area of 2.56 per cent in the entire period of the study. Other crops that were found to have performed well were coconut and banana and other plantains. The trend break analysis was also used to study the growth performance of crops in Kerala. It was established that the main reason for breaks in areas under most crops was related to profitability. It was noted that with increases in prices, especially for plantation crops, the areas under the crops also increased. Several crops had breaks in their areas exactly at the time when rubber was recording its best prices in 1995 and from 2007-08 to 2009-10. Due to the sustained rise in prices of rubber, crops like paddy were much affected, since it is a labour-intensive crop and an increase in the wages of field labour impacted it negatively. Plantation crops on the other hand were less labour intensive and the increases in prices were an incentive for the farmers to expand the area under the cultivation of those crops. In turn, the area under rubber and coconut increased tremendously especially from late 1980s. Thus, changes in prices was a major cause for most breaks in most of the crops. The government policies and interventions in the agricultural sector were found to be the major contributor to the trend breaks. The introduction of policies that promoted the cultivation of crops such as paddy led to increase in their areas and these led to the breaks. These include the group farming scheme implemented in 1988-89, promotion of paddy cultivation in the fallow lands of 2004-05 and the enactment of the conservation of paddy land and wetland act of 2008. Seemingly Unrelated Regression (SUR) model was used to understand the determinants of the growth in the GSDP from agriculture. The model revealed that the largest contributor to per capita agricultural GSVA was gross cropped area irrigated. This meant that for every additional hectare of land brought to irrigation every year, it added 75.3 per cent of the value of the output per hectare from the crop to the per capita income from agriculture. The area under high value crops was also found to be significant and positively influencing per capita GSVA from agriculture and this was a confirmation that increased cultivation of high value crops was key to growth in earnings from agriculture in Kerala State. The fertiliser consumption was also found to be important in improving the earnings realised from agriculture. The use of fertilisers improved the contribution from crops to the agriculture per capita income by 24 per cent, while rainfall improved the earnings by 10.5 per cent. The study on the land use and cropping pattern changes revealed through transition probability matrices obtained from Markov chain analyses showed that various land use classes apart from forest were not stable throughout, but had seasons of loss and gain to and from other different land use classes, which was also the same case for crops. For instance, area put to non-agricultural uses and, permanent pastures and grazing lands were the most stable land use classes in the first phase of the study (1970-71 to 1980-81). This phase was the beginning of shift from various crops to plantation crops and large tracts of lands in which other crops were grown were converted to the cultivation of plantation crops like rubber due to increasing prices and profitability. This shift in land use helped to increase the net sown area. The net sown area also had 4.4 per cent probability of receiving land from permanent pastures and grazing lands, and a probability of 21.4 per cent from area under miscellaneous tree crops. However, in contrast to the first phase, in the last and sixth phase from 2011-12 to 2018-19, the area under non-agricultural uses which got a stimulus in the fifth phase, showed a 100 per cent probability of holding to its share in the sixth phase. The cultivable waste drew gains from net sown area. The area under pastures showed a 93.2 per cent of probability of transition to net sown area, an indication that the area under permanent pastures and grazing lands was getting cleared and converted to farm lands. The study on economics, efficiency and profitability of crops revealed that the profitability of tapioca, coconut and black pepper were highest in the large and medium holdings. Ginger presented an inverse of the trend shown in many crops in which small holdings were the least profitable. This was attributed to complexities in managing large ginger farms which were prone to diseases. Overall, the profitability improved over the period under study for all the crops other than ginger, which could be attributed to increased use of high yielding varieties and efficient fertilisers that improved the productivity. Banana and other plantains, autumn paddy and coconut had the largest TFP. The TFP growth in these crops were majorly driven by increases in their technological components. Winter paddy and summer paddy also had a slight increment in their TFP mainly driven by growth in their technological changes and hindered by decline in their technical efficiencies. Tapioca, black pepper and ginger recorded negative changes in their TFPs. The decline in TFP of these crops was majorly due to the decline in the technological changes in these crops. Therefore, there is need for increased public investment in the agricultural sector. The increased public investment will in turn promote private investment in potential areas of the state, which will sequentially help to provide incentives and favourable environment for agricultural development.ThesisItem Open Access Divergence studies in salad cucumber (cucumis sativus L)(Department of Olericulture, College of Agriculture, Vellayani, 2006) Smitha Sara, Abraham; KAU; Gopalakrishnan, T RThe present investigation on “ Divergence studies in salad cucumber (Cucumis sativus L)” was conducted at College of Horticulture, Vellanikkara, Thrissur during December 2005- April 2006. Twenty-eight salad cucumber genotypes collected from different parts of India were utilized for the study. The extent of variability, correlation between yield and its component characters, path analysis and divergence among 28 genotypes were assessed. The 28 genotypes were significantly different for 15 characters studied. The genotype Phule Himangi (20.22 kg/plot) emerged as high yielder followed by AAUC 2 (15.11 kg/plot). Selection of plants based on yield/plot was observed to be efficient than selection of component characters. All the accessions were prickled on the surface. All were monoecious and produced yellow flowers. Most of the genotypes produced light green fruits whereas Phule Himangi produced white stout fruits. Genotypes CS 25 and CS 35 were comparatively free from biotic factors except mosaic and serpentine leaf miner. Total yield per plot showed positive correlation with fruits per plant, duration of crop and number of harvests. Negative correlation was observed between total yield per plot and number of branches. It is clear from the study that, for obtaining higher yield characters like fruits per plant, duration of crop, number of harvests etc should be considered in the selection programme. Fruits per plant had maximum positive direct effect on total yield per plot. Higher positive effects for days to first male flower anthesis was observed on total yield per plot. The genotypes were grouped into 5 clusters based on Mahalanobis D2 statistics. Cluster I, II, III, IV and V contained 13,8,4,2,1 genotypes respectively. Inter-cluster distance was maximum between cluster II and V (48733.77) and minimum between cluster I and III (8415.55). Cluster V showed maximum average inter-cluster distance with any another cluster.ThesisItem Open Access Socio-economic vulnerability and adaptive strategies to environmental risk: a case study of water scarcity in agriculture(Department of agricultural economics, College of horticulture, Vellanikkara, 2012) Rinu T, Varghese; KAU; Indira Devi, PWater stress is predicted as one of the most pronounced risk of climate change in countries like India. Kerala is reported as moving from wetness to dryness. Management of risks of climate change necessitates scientific estimates of the level of potential damage, accommodating for the vulnerability and adaptive mechanisms of the communities. The study entitled ‘Socio-Economic Vulnerability and Adaptive Strategies to Environmental Risk: A Case Study of Water Scarcity in Agriculture’ was undertaken with the objectives of measuring farmers’ vulnerability to water stress in agriculture and its impact on household welfare and to identify and assess the relative influence of various factors on the level of vulnerability. Further, short term and long term adaptive strategies to water stress among farmers of different socioeconomic conditions were also analysed. The most backward district of the state of Kerala, Wayanad was selected as the study area. Multistage random sampling method was adopted for sample selection. Nine panchayats from four Community Development Blocks were selected, from each of which, 15 farmers were selected. Thus the total sample size was 135. Primary data regarding the socio-economic status, land use pattern and production, sources of water for domestic use and irrigation, perceptions and adaptive strategies to water scarcity were gathered using pretested interview schedule. Indicator based approach was used for constructing the composite vulnerability index to assess the vulnerability level of the farmers. Logit model was employed to identify the factors influencing vulnerability. Apart from these, conventional tabular analysis was also used. The cropping pattern in Wayanad shows a clear shift in favour of commercial crops like arecanut, banana and rubber. The conversion of paddy lands for these crops was to the tune of 41 per cent during the last decade. The area under pepper shows a decline (54 %) and that of other commercial crops show an increase. Among other reasons, climate change is perceived as one of the major reasons for this decision by the farmers. The analysis of weather parameters and climate predictions for Wayanad also supports the farmer level observation. The rainfall and temperature pattern of the district during past years indicate an increasing level of water stress. Climate change models project very high variation in the rainfall pattern of the district in future years. An increase in the average annual rainfall coupled with lower levels of summer showers are predicted. By 2020, summer showers may decline to 43.6 mm as against the present, 70 mm. High intensity rains with low duration will be the major characteristic. A gradual increase in annual temperature by about 1.5ºC is also predicted. In this background, a composite vulnerability index considering social, economical and agronomic factors of the farmers was constructed to measure the vulnerability. More than 50 per cent of farmers were highly vulnerable and the proportion of the farmers in that group was found to be increasing during the past five years. An inverse relationship was observed between the land holding size and vulnerability level, three- fourth of the marginal farmers were vulnerable while most of the small and large farmers (41.27 % and 34.78 % respectively) belonged to the other group. Thavinjal panchayat of Manathavady block was found to be the most vulnerable and Muppainad and Vythiri panchayats of Kalpetta block were found to be the least vulnerable. The results of the logit model shows that five out of eight factors viz. diversity index, cropping intensity, percentage of irrigated area to total cropped area, net cropped area and education as having significant influence on the probability of an agricultural household being vulnerable, of which the diversity index and cropping diversity are the most influential factors. Farmers often have their own adaptive mechanism to cope with the water stress condition within the constraints. In general, adaptation strategies followed in domestic and agricultural sector can be classified into supply management strategies and demand regulating strategies or long term and short term strategies. The supply management programme includes those activities which ensure the steady supply of water and the demand side management mainly focus on more efficient use of available water resources and improving water resources. Among the respondents, a gradual shift from the dependence on external sources of water to owned sources has occurred. The dependence on external sources increases the time spent and drudgery of women folk in such households. Common adaptation strategies followed by the farmers include irrigation, varietal selection, mixed cropping, crop diversification, organic farming, soil and water conservation measures (mulching, earthen bunds and rain pits) and migration (geographical and sectoral). About 39 percent of the sample respondents were adopting irrigated farming and the average expenditure was found to be Rs 18187 per household which is nearly nine percent of the total household income. Only a few farmers were adopting micro-irrigation methods because of its high investment. This cost of adaptation, further reduces their consumption expenditure leading to household welfare loss. The study suggests research interventions in developing a sustainable cropping pattern and scientific validation with location specific studies on the impact of climate change on major crops. The need for empowering the farmers through technology, infrastructure, financial and extension support to adapt to water stress is also underlined. It highlights the importance of water resource development and the need for identifying the constraints in the adoption and develop/modify the technologies to suit local conditions. Further the implementation of weather based crop insurance programmes with localised meteorological stations as reference points is also stressed.ThesisItem Open Access Molecular marker development for cassava mosaic disease resistance using bioinformatics tools(Department of Plant Biotechnology, College of Agriculture, Vellayani, 2015) Ambu, Vijayan; KAU; Sreekumar, JThe study entitled “Molecular marker development for cassava mosaic disease resistance using bioinformatics tools” was conducted at ICAR-CTCRI, Sreekariyam, Thiruvananthapuram during October 2104 to October 2015. The objectives of the study included development and evaluation of various SNP and SSR prediction pipelines, computational prediction and characterization of SNP and SSR in cassava, verification of SNP and SSR markers for cassava mosaic disease (CMD) resistant and susceptible breeding lines. The preliminary data set for the identification of SSR and SNP markers was obtained from the EST section of NCBI and the cassava transcript sequences from the Phytozome. A total of 120461 sequences was classified into 20 cultivars. The dataset was reduced to 14336 sequences after several pre-processing and screening steps. The resulting sequences were assembled and aligned using CAP3 and 2088 contigs were obtained. SNPs and SSRs were predicted from these datasets using respective prediction tools. The SNP prediction tools such as QualitySNP and AutoSNP were compared for their performance. Analysis was performed to identify the tool with the ability to annotate and identify more viable nonsynonymous and synonymous SNPs. The SSR prediction tools such as MISA and SSRIT was compared for their performance. Analysis was performed to identify the tool having the ability to predict more viable SSRs and the ability to classify them as mono, di, tri, tetra, penta, hexa and poly SSRs. Using QualitySNP, thirty nonsynonymous SNPs and twenty-six synonymous SNPs were identified. Using MISA, n 217 mono SSRs, 132 di SSRs, 139 tri SSRs, 3 tetra SSRs, 1 penta SSRs, 3 hexa SSRs and 42 complex SSRs were identified. Five sequences from identified SNPs and SSRs which have high hit percentage were selected for validation and primer designing for CMD resistant genes. These primers were validated using 5 resistant and 5 susceptible cassava varieties. Among the 10 primers after validation in wet lab, one SNP (SNP896) and one SSR (SSR 2063) primer was able to clearly differentiate between the resistant and susceptible varieties which can be used as potential markers in the breeding program for screening CMD resistance in cassava.
