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ThesisItem Open Access Calibration and validation of ceres rice crop simulation model(Department of Agronomy College of Agriculture, Vellayani, 2018) Anju, V S; KAU; Girija Devi, LThe project entitled ‘Calibration and validation of CERES-Rice crop simulation model’ was conducted in the Department of Agronomy, College of Agriculture, Vellayani from 2016 to 2018 with the objectives to calibrate and validate CERES-Rice model to generate the genetic coefficients of the rice variety Prathyasa, to study the crop- weather relationship and to quantify the yield gap of the variety by running simulations. The field experiment was conducted at Upanniyoor Panchayat in farmer’s field for four seasons (Virippu 2016 and 2017 and Mundakan 2016 and 2017) and it was laid out in randomized block design. The treatments consisted of five dates of sowing each in Virippu (D1 - May 31, D2 –June 15, D3 –June 30, D4 –July 15 and D5 – July 30) and five dates of sowing each in Mundakan (D1 -Oct 14 / Sept 26, D2 – Oct 30 /Oct 10, D3 – Nov 14/ Oct 25, D4 –Nov 30/Nov 9 and D5 – Dec 14/Nov 24). The sowing dates in Mundakan seasons of 2016 and 2017 varied due to the delayed onset of rainfall in 2016. The plot size was 5 x 4 m2 with three replications. Routine observations on height, leaf area, dry matter production (DMP), number of tillers, panicles, spikelets per panicle, filled grains per panicle, 1000 grain weight, straw yield and grain yield were recorded apart from phenological observations. Soil analysis was conducted before and after the experiment. The soil and crop data collected from the experimental field and weather data from the Department of Agrometeorolgy were used as inputs for running the model. Study on phenology revealed that the crop duration decreased from 111 to 100 and 117 to 107 days respectively in Virippu 2016 and 2017. A similar decreasing trend was observed in Mundakan 2016, but in Mundakan 2017, it increased from 114 to 117 days in early sowing and decreased drastically from 117 to 105 days in delayed sowing. The height of the plant was found varying at different stages, D1 produced the tallest plants at harvest in Virippu seasons of both the years, while it was the highest in D3 in Mundakan 2016 at different stages and D5 in Mundakan 2017. The number of tillers was the highest in D2 and D1 respectively in Virippu in both the years and D2 and D1 respectively in Mundakan 2016 and 2017. The DMP was the highest in D2 and D1 respectively in Virippu and Mundakan 2016 and 2017. The grain yield was the highest in D2 in both the seasons in 2016 and D1 in both the seasons in 2017. The yield attributes such as productive tillers m-2 was the highest in D2 in both the seasons in 2016 and D1 in both seasons in 2017. The number of spikelets per panicle was the highest in D1 during Virippu 2016 and 2017 and Mundakan 2017 and D2 in Mundakan 2016. D1 in Virippu 2017 was on par with D2, and D2 in Mundakan 2016 was on par with D1. The number of filled grains per panicle was higher in D1 in Virippu 2016 and Mundakan 2017, while D2 recorded higher filled grains per panicle in Virippu 2017 and Mundakan 2016. D1 in Virippu 2016 was on par with D2 and D2 in Mundakan 2016 was on par with D1. The harvest index (HI) was higher in D1 in Virippu 2016 and 2017 and Mundakan 2017, while D2 recorded higher HI in Mundakan 2016. D1 in Virippu 2016 was on par with D2 and D5, and D1 in Virippu 2017 was on par with D2. In Mundakan season, D2 was on par with D1 and D5 in 2016 and D1 was on par with D2 in 2017. In Virippu and Mundakan 2016, N uptake was the highest in D1 while P and K uptake were the highest in D2, whereas in Virippu and Mundakan 2017, N, P and K uptake were the highest in D1. The organic carbon content of the soil was found influenced only after Virippu 2016 with D5 recording the highest value. In the case of available N, P and K status of the soil, only the N status was found affected and that was only after Mundakan 2017 with D2 recording the highest value. Crop weather relationship was studied by computing the different heat units such as Growing degree days (GDD), Heliothermal units (HTU), Photothermal units (PTU) and Heat unit efficiency (HUE) at different stages such as sowing to active tillering (P1), active tillering to panicle initiation (P2), panicle initiation to booting (P3), booting to heading (P4), heading to 50% flowering (P5), 50% flowering to physiological maturity (P6), vegetative stage (P7), reproductive stage (P8) and ripening stage (P9). These heat units computed were the highest in D1 and showed positive correlation with yield for GDD at P1, HTU at P5 and P6, PTU at P1 in Virippu, while in Mundakan positive correlation was obtained with GDD at P1 and P7, HTU at P2, PTU at P1 and P7. Negative correlation was obtained with GDD at P3, P8 and P9 and PTU at P4, P8 and P9 in Virippu and with HTU at P2 and P3 and PTU at P6 and P9 in Mundakan. The correlation between yield and yield attributes with weather parameters revealed positive correlation for minimum temperature at P3, P4 and P8, RH I & RH II at P1 and P7, BSS at P2, P3, P4 and P6, rainfall and rainy days at P1 and P7, pan evaporation at P6, P8 and P9 and wind speed at P6 and P9 in Virippu season. Negative correlation was observed with minimum temperature, pan evaporation and wind velocity at P1, rainfall at P3, P4, P6, P8 and P9, rainy days at P6, P8 and P9, RH I at P6, P8 and P9, RHII at P5, P6 and P9 in Virippu season. In Mundakan season positive correlation was obtained with maximum temperature from P1 to P9 except P6, RH I at P2, P4, P5, P7, P8 and P9 and rainy days at P1 and negative correlation with maximum temperature at P1, P2 and P6, minimum temperature at P6, BSS at P2, rainfall at P6, pan evaporation at P3, P5, P6, P7, P8 and P9. The genetic coefficients for the variety Prathyasa was generated by calibrating the CERES-Rice model by using the data of Virippu rice 2016 and validated by using the data of Mundakan 2016, Virippu and Mundakan 2017 respectively and the genetic coefficients generated were P1-720, P2R-33.7, P5-21.3, P2O-12, G1-38.7, G2-0.028, G3-1, G4-1 respectively. Model simulated results showed that there was close association between observed and simulated yield and the error percentage varied from -16.90 to 16.55 for Virippu 2016 and from -1.26 to 64.77 in Virippu 2017. In Mundakan, error per cent ranged from -13.43 to 16.63 in 2016 and from -11.09 to 12.58 in 2017. The error percentage for panicle initiation day varied from 1.96 to 18.37 in Virippu 2016, while it varied from -5.88 to 10 in Virippu 2017 and for Mundakan it varied from -8.16 to 0 in 2016 and from -8 to 1.96 in 2017. Similarly the error percentage for anthesis day varied from 8.54 to 11.25 and 4.88 to 9.88 in Virippu 2016 and 2017 and from 4.88 to 9.88 and 3.66 to 8.43 in 2016 and Mundakan 2017. The error percentage of physiological maturity day varied from -1.96 to 18 in Virippu 2016, while it deviated from -7.84 to 0.99 in Virippu 2017. During Mundakan season, error percentage ranged from -1.98 to 6.54 in 2016 and from -2.91 to 4.81 in 2017. Regression equations for grain yield were developed for certain phenological stages in Virippu and Mundakan from highly correlated weather parameters. The yield gap quantification revealed that the highest total and sowing yield gaps were in delayed sowing (D5), management yield gap in early sowing(D1), and the lowest in D3 and D4 (delayed sowing) and D2 (early sowing) respectively for the same parameters. Thus, the study enabled to generate the genetic coefficients of variety ‘Prathyasa’ and simulated the grain yield and panicle initiation, anthesis and physiological maturity days with minimum error percentage. The study also helped to quantify various yield gaps such as total, management and sowing gaps due to different dates of sowing, from the potential yield generated by the model along with the attainable and actual yield data supplied from the field experiment and farmers’ field. The various correlations worked out between yield, weather parameters and heat units provided an insight into the crop weather relationship. Finally, and the foremost implication of the study is that delayed sowing reduces the yield considerably in rice crop in both the seasons irrespective of other factors.ThesisItem Open Access Management and utilization of water hyacinth (Eichhornia crassipes ( Mart.) Solms)(Department of Agronomy College of Horticulture, Vellanikkara, 2018) Indulekha, V P; KAU; George Thomas, CWater hyacinth is one of the most productive plants on earth, but it is also considered as the world’s worst aquatic weed. The phytoremediation capacity of water hyacinth and its management through ecofriendly means like silage making, composting, and mulching were studied at the College of Horticulture, Vellanikkara. The phytoextraction capacity of water hyacinth was evaluated through a purposive sampling by collecting plant and water samples from 20 sites in central Kerala. These samples were analysed for various nutrients including heavy metals. To study the association of plant nutrients with water nutrients, cross tabulation was done and dependence of plant nutrient factor on water nutrient was measured through Chi-square. The Chi-square statistic was significant for N, P, Mg, and Ni indicating that the level of nutrients could be brought to a minimum through water hyacinth. The accumulation of heavy metals in water hyacinth was in the order Fe> Al> Mn> Zn> Cr> Ni> Co> Hg> Pb> As. Among them, Pb content in plant samples was within the permissible limit, but contents of Fe, Cu, Cr, Zn and Ni were beyond the safe limits. The quality and palatability of silage prepared with fresh and wilted water hyacinth with or without rice straw or guinea grass and using molasses, cassava flour, or rice bran as additives was investigated. Considering the quality parameters such as pH, odour, and palatability, wilted water hyacinth with molasses (5%) or cassava flour (10%) and wilted water hyacinth with cassava flour (10%) and rice straw (10%) or guinea grass (10%) are the best options for utilizing water hyacinth as silage. The composting experiment consisted of four methods viz., Bangalore method, Indore method, phospho-composting, and vermicomposting. All the prepared composts had neutral to slightly alkaline pH. The lowest C: N ratio was recorded with vermicompost (11.58) followed by Bangalore compost (12.68). Nitrogen content at 3 months after composting (MAC) was higher in vermicompost and Bangalore compost. The highest N content at 6 MAC was observed in vermicompost (1.75%). Phosphorus content was higher in phospho-compost at 3MAC and 6 MAC. There was no significant difference in K content of different composts at 3 MAC. Calcium, Mg and S contents were high in vermicompost. Micronutrients such as Zn, Cu, Co, and Ni were higher with Bangalore composting. Heavy metals such as As, Cd, and Pb were not detected in any of the composts. None of the composts contained heavy metals beyond safe limits. A field experiment involving three mulch materials–jack tree leaves, green water hyacinth, and coconut leaves–were compared with no mulching in turmeric for two years. All the mulch materials including water hyacinth had positive effects on most morphological and physiological parameters of turmeric such as plant height, number of leaves, leaf area index, leaf area ratio, and dry matter production. In both years, rhizome yield was also higher in plots mulched with organic debris compared to non-mulch control. Nutrient uptake by the crop was also higher with mulching compared to non-mulched plots. All the mulch materials substantially affected weed density and weed dry weight and reduced turmeric-weed competition for different growth factors.ThesisItem Open Access Silicon availability of tropical soils with respect to Rice nutrition(College of Horticulture, Vellanikkara, 2016) Arya Lekshmi, V; KAU; Jayasree Sankar, SSilicon (Si) is the second most abundant element in soil. The amount of silicon in soil depends on parent material, soil type, pedogenic process and landscape. In soil solution, Si is present as monosilicic acid which is the only form that the plant can absorb from soil. The productivity of rice is comparatively low in soils of Kerala. As a ̳Si – accumulator‘, rice can benefit from Si nutrition. The application of Si can enhance growth and yield of rice. With this background, studies were conducted to categorize major rice growing soils of Kerala according to plant available silicon and to evaluate the efficacy of different sources of silicon including rice straw in wetland rice. The release of silicon from different soils added with various silicon sources under different water regimes was also monitored. Soil samples were collected from five different locations representing major rice growing regions of Kerala viz., Kuttanad, Kole land, Pokkali, sandy and lateritic to categorize them according to plant available silicon. The available Si ranged from 7.70 mg kg -1 (sandy soil) to 34.91 mg kg -1 (Kole land soil) in the order Kole land > Pokkali > lateritic > Kuttanad > sandy soil. All the soils under study were categorized as low in available Si. The available Si had positive correlation with organic carbon, available N, Ca, Mg, Fe, Mn, Zn, exchangeable K, Ca, Mg and CEC and negative correlation with available boron, AEC and silica-sesquioxide ratio. These soils were subjected to fractionation of silicon. The major fractions of Si were mobile, adsorbed, organic, occluded, amorphous and residual Si. The percentage distribution of fractions of Si in these soils were in the order; residual Si > amorphous Si > occluded Si > organic Si > mobile Si > adsorbed Si. Quantity – intensity relationship of five major rice growing soils at two temperatures viz. 25 0 C and 40 0 C were studied. The highest buffer power was indicated by Kuttanad soil followed by Pokkali and sandy soils at 25 0 C. It clearly indicated that these soils have a higher power to retain Si on solid phase and replenish its concentration in soil solution as and when it is depleted through plant uptake or leaching. The equilibrium Si concentration and the amount of Si adsorbed by each soil were used to test the fitness of data to the adsorption isotherms viz., Langmuir, Freundlich and Temkin. The data obtained from the adsorption experiments fitted into Freundlich and Temkin equations, but not to Langmuir equation at 25 0 C. At 40 0 C no adsorption equations were obtained for any soil.An incubation study was conducted to know the extent of release of Si on addition of different sources of silicon such as rice husk ash, biodecomposed rice husk, calcium silicate and sodium silicate in five rice growing soils under submerged water regime (SWR) and field capacity water regime (FCWR). Addition of Si significantly increased the release of available Si in all soils except Kole land soil after a month. Kole land soil showed higher release of available Si after two months. The highest release of available Si was at SWR in case of Kole land and Kuttanad soil, where as Pokkali, sandy and lateritic soils showed more release of available Si at FCWR. Irrespective of soils, treatment with sodium silicate showed higher release of available Si. Total Si showed a decreasing trend over the period of incubation for three months in all the soils. A field experiment was conducted at Agronomic Research Station, Chalakudy to evaluate the efficacy of different sources of silicon including rice straw in wetland rice. Rice husk ash, biodecomposed rice husk, calcium silicate and sodium silicate were used as source of Si along with fertilizers as per package of practice recommendation (NPK alone). The maximum number of panicles per hill, number of spikelets per panicle, thousand grain weights and minimum number of unfilled grains per panicle were recorded in treatment with calcium silicate application. The maximum grain yield of 6.90 t ha -1 was recorded in treatment T 5 (T 2 + Calcium silicate) and significantly superior (fig.54) over all other treatments. This increase in yield may be due to the effect of application of Si on soil fertility, nutrient uptake, and plant growth. The direct effect of Si fertilization on increased number panicle per hill, number of spikelets per panicle, and thousand grain weight and decreased number of unfilled grains per panicle might be the reason for increased grain and straw yield in treatment with calcium silicate. The treatment with POP + sodium silicate showed the highest uptake of Si by grain and straw of rice. The sources of Si had no residual effect on grain and straw yield of succeeding rice crop. In general, sandy soil low in available Si had a high response to applied Si in achieving higher grain yield.