Agriculture

An innovative approach incorporated to predict large quantity of rose water extraction through distillation process in a solar still under the climatic conditions of Coimbatore, India (11?.00 N, 77 ? E). The rose water extraction to utilize the process is performed to increase the large yield rate by coupling the still with the concentrating assembly. The hourly rose essence yield from the still and efficiency are recorded. In normal evaporation process of the still requires more time to evaporate the content in the still which spends 1½ hour for evaporation. But in this type of still concentrator plays a major role for evaporating the content within a short duration of time. It requires only 10 minutes for evaporation. Thus this type of still is very much support to extract the rose essence without any loss of smell. The conventional solar still loses its smell even before the evaporation process.

Different concentrations of vermicompost such as 25% (25kg vc: 75 kg red soil), 50% (50kg vc:50 kg red soil), 75% (75kg vc:25 kg red soil), and 100% (100 kg vc) produced by earthworm, Eudrilus eugeniae, were added to red soil in polythene bags. Maize plants (Zea mays L.) were grown on vermicompost-enriched soil for 90 days. The plants were harvested at the end of 90 days and the kernels were collected. In the present investigation the number of kernels was counted; the length, breadth, and circumference of kernels were measured; the weight of kernel and total weight of all kernels were calculated. The maximum kernel number of 598.55/corn and the highest length of 1.71cm/kernel were noticed in the plants cultivated on 75% vermicompost concentration whereas the maximum kernel breadth of 1.40cm/kernel, circumference of 3.07cm/kernel, weight of 0.41 gm/kernel and total weight of all kernels (232.43gm/corn) were noticed in the plants grown on 50% vermicompost concentration. At the same time minimum kernel number of 69.42/corn, low kernel length of 0.80cm/kernel, breadth of 0.60cm/kernel, circumference of 2.02cm/kernel, weight of 0.08gm/kernel and total weight of all kernels (5.55gm/corn) were found in the control plants. The present investigation clearly revealed that the addition of vermicompost to soil greatly enhanced the kernel yield in maize.

In India maize ranks fifth position in area and fourth in production among the major cereals grown. Being a C4 plant and having very high yield potential, it is called queen of cereals. One of the main deterrents to high grain yield in maize is its susceptibility to several diseases. Of 112 diseases of maize reported so far from different parts of the globe, 65 are known to occur in India. Banded leaf and Sheath blight (BLSB) is one of them caused by most widespread, destructive and versatile pathogen Rhizoctonia solani f. sp Sasakii (teleomorph: Corticium sasakii, syn Thanatephorus cucumeris) which claims significant yield loss (Saxena, 2002). It was first reported by Bertus (1927) in Sri Lanka under the name Sclerotial disease. The epidemics of this disease were first reported in the warm and humid foot hills of Himachal Pradesh by Thakur et al. (1995). Singh and Sharma (1976) recorded a loss in grain yield in the range of 11 to 40 per cent due to this disease while Lal et al. (1985) reported a reduction to the extent of 97.4 per cent in severe condition. A range of 25 to 30°C (Ahuja and Payak, 1981) coupled with an average relative humidity of 90-100% (Ahuja and Payak, 1981) is most suitable for development of this disease. These conditions prevail in the plains of N.E. region of India during the months of July-August, a time when the crop is in vulnerable growth stage. In India the disease was first recorded in the Tarai (foot hill plain areas) region of Uttar Pradesh (Payak and Renfro, 1966). Maize plant is affected by as many as 61 diseases, out of which 16 have been identified a major ones which occur both in tropical and temperate regions of India (Sharma and Payak, 1986). Among these, banded leaf and sheath blight (BLSB) incited by Rhizoctonia solani is gaining economic importance. Grain yield loss, depending on severity varies between 11 to 40 per cent (Singh and Sharma, 1976). Now banded leaf and sheath blight is considered as one of the major diseases of Maize (Payak and Sharma, 1985).

Any experiment should be compatible with scientific approaches such as statistical reliability, rationality, internal and external validity, generalizability, and other specific criteria related to the subject, at the laboratory level or field level. Hence, planning a design to accomplish those approaches is critical, and selecting an appropriate model accordingly to acquire basic principles of design would make the experiment more precise. Straightforward designs of CRD, RCBD, and LSD, and complicated designs of IBD, Factorial experiments, Split-plot designs, and Lattice designs are discussed here. Consequently, the researcher can decide the appropriate experimental design.

Climate change and climate variability and financial institutions’ unwillingness to give loans have resulted in many farmers losing confidence in dry land agriculture. Traditional crop insurance methods have also presented challenges due to the risk related to adverse selection and moral hazard resulting in high transaction costs for individual assessment. This study focused on developing a weather index based insurance model that uses artificial neural networks to estimate potential evapotranspiration (ETO) and consequently yield reduction due to moisture stress. Weather data from 2012 to 2015 for Kutsaga area in Harare was used for the study. Seasonal weather data were used as input data to the first model to predict the ETo. The output ETO and effective rainfall data together with the crop factor (Kc), yield reduction factor (Ky), root zone depth (RzD) and root zone moisture (RzM)were used as input data for the second network to compute % yield reduction. Data for maize for the 2012-13 growing season was used for training the network and validating the estimated ETO and % yield reduction. The estimated ETO compared very well with the calculated values with R2 values of above 0.84. The estimated yield reduction % indicated even high accuracies with R2 values of above 0.91. The 2014-2015 growing season resulted in crop loss due to mid-season dry spells and the model predicted a 100% crop loss which means the farmer had to be compensated for the value equivalent to cost of inputs. The model has got potential to be used by insurance companies using weather based data and, with mobile banking transaction costs can be reduced.Climate change and climate variability and financial institutions’ unwillingness to give loans have resulted in many farmers losing confidence in dry land agriculture. Traditional crop insurance methods have also presented challenges due to the risk related to adverse selection and moral hazard resulting in high transaction costs for individual assessment. This study focused on developing a weather index based insurance model that uses artificial neural networks to estimate potential evapotranspiration (ETO) and consequently yield reduction due to moisture stress. Weather data from 2012 to 2015 for Kutsaga area in Harare was used for the study. Seasonal weather data were used as input data to the first model to predict the ETo. The output ETO and effective rainfall data together with the crop factor (Kc), yield reduction factor (Ky), root zone depth (RzD) and root zone moisture (RzM)were used as input data for the second network to compute % yield reduction. Data for maize for the 2012-13 growing season was used for training the network and validating the estimated ETO and % yield reduction. The estimated ETO compared very well with the calculated values with R2 values of above 0.84. The estimated yield reduction % indicated even high accuracies with R2 values of above 0.91. The 2014-2015 growing season resulted in crop loss due to mid-season dry spells and the model predicted a 100% crop loss which means the farmer had to be compensated for the value equivalent to cost of inputs. The model has got potential to be used by insurance companies using weather based data and, with mobile banking transaction costs can be reduced.

In order to better understand the zootechnical and economic characteristics of piglet farming, a diagnostic study was conducted from October to December 2019, three months with 30 pig farmers, in the form of cross-sectional and retrospective surveys in the three municipalities of the town of Kindu in the Democratic Republic of Congo. The results showed that pig farming was practiced by both married and educated men and women. They were mainly zimba, Kusu and Nande and associated livestock with agriculture, private activity or trade. They operated in two different livestock systems (traditional and semi-intensive), with a clear predominance of the traditional system (85.5% of farms). The latter was characterized by semi-intensive hog barns (51.1%), where local-bred pigs were mostly reared (90.4% of farms). The average herd size was 19.2 - 20.1 pigs. In 97% of cases, farmers fed the animals from kitchen waste and agricultural and agro-industrial by-products. The average breeding age was 7.6 -1.8 months and the average litter size was 7.4 - 2.2 piglets. This livestock subsector has real potential for poverty reduction because it generates net incomes on average of $40 per piglet, depending on the system. Nevertheless, the lack of training of herders, inadequate pig habitats, food and health constraints remain the main factors hindering the development of pig farming in this city. It would be useful to find alternative ways to improve the habitat conditions, feeding and health monitoring of pigs, accompanied by better organisation and capacity building for producers and other players in the hog sector.

The objective of the study was assessment and mapping of the community villages in order to rank degree of vulnerabilities to climate change. On sensitivity to vulnerability, the majority of the respondents indicated that it was high (59.6%), medium (16.3%) and low (20.8%).of which on response to exposure to vulnerability, the respondents who indicated high (61.7%), medium (24.2% and low (11.3%).On vulnerabilities in response to adaptive capacity to vulnerability, was high (3.3%), medium (12.5%) and low (81.3%).This study demonstrated that participatory approach of addressing vulnerability to climate change which involved all stakeholders is effective in this dry forested pastoral ecosystem.

The aim of this investigation was the ability of different indices to identifies drought resistant genotypes of cotton under normal and stress conditions. Thirteen drought tolerance indices i.e., stress susceptibility index (SSI), tolerance index (TOL), mean productivity index ( MP), geometric mean productivity (GMP), stress tolerance index (STI), yield index (YI), yield stability index (YSI), drought resistance index (DI), yield reduction ratio (YR), a biotic tolerance index (ATI), stress susceptibility percentage index (SSPI), harmonic mean (HM) and golden mean (GOL) were calculated based on seed cotton yield/plant under normal (Yp) and stress (Ys) conditions for 24 cotton genotypes over the two summer seasons (2015 and 2016) at Sakha Agriculture Research Station, Agriculture Research Center, Kafr El-Sheikh, Egypt. The values of mean performances showed that, most studied genotypes were better than the grand mean during Yp and Ys. Drought stress reduced the studied traits while other was tolerant to drought, suggesting genetic variability in 24 cotton genotypes for drought tolerance. According to drought tolerance indices, MP, GMP, STI, YI and HM under Yp and Ys as well as the other studied drought tolerance indices under Ys could properly distinguish drought tolerant cotton genotypes with high yield performance. Therefore, the indices of MP, GMP, STI, YI and HM were considered as a better predictor of Ys and Yp than the other indices. Screening drought tolerant genotypes using mean performances and drought tolerance indices showed cleared that the genotypes G.94, G.86, G.96 and G.89 were the most drought tolerant under Yp and Ys. Thus, they are recommended to be used as a parents for improvement of drought tolerance of cotton in breeding and hybridization programs in Egypt to produce and select a new recombination’s are more and more tolerant for drought to overcome the water shortage and reduce the water rating of the cotton crop. And the possibility of planting and expansion of those new recombination’s under the conditions of new lands that suffer from shortage of water and access to an economic crop. Cluster analysis based on all studied traits of 24 cotton genotypes into five and nine clusters under normal and drought stress conditions, respectively .The results for cluster analysis suggested that these genotypes could be used as a source of germplasm for breeding for drought tolerance and also cleared that the hybridization between clusters may increases variability and expected transgresive segregation to select the new germplasm had more and more drought tolerance. 

Pigeon pea [Cajanus cajan (L.) Millsp.] is the rich sources of dietary protein, carbohydrate & certain minerals but its poor yield needs to formulate a nutrient management practices for higher productivity in upland alfisols of Tripura and to combat it, an experiment was conducted during three consecutive kharif seasons at College of Agriculture, Lembucherra, Tripura comprising of two levels of Lime (150 and 200 kg ha-1) in main plot and two levels of molybdenum seed treatment (0 and 4g kg-1 of seed) in sub plot and 4 levels of recommended doses, viz 0% of RDF ha-1, 50% of RDF ha-1, 75% of RDF ha-1 and 100% of RDF ha-1 (Recommended dose @ 20:60:40 of NPK). It was revealed that by the use of 100% RDF ha-1 with 150 kg ha-1 lime and 4g kg-1 of molybdenum seed treatment leads to higher yield and return per rupee respectively. But by the use of 100% RDF ha-1 with 200 kg ha-1 lime and 4g kg-1 of molybdenum seed treatment, return per rupee was recorded at 2.98. The investigation reflects that by the use of 75% RDF ha-1 return per rupee (3.78) was less compared to 100% RDF ha-1 but significant with saving the cost of 25% RDF ha-1 and soil health from the excessive use of fertilizer for sustaining the agricultural growth.

Banana bunchy top virus (BBTV) is a destructive pathogen in banana cultivating areas worldwide. As the virus is located in the phloem tissue and its purification is difficult and gives low yield. Therefore, in the present study BBTV coat protein was bacterially cloned and expressed using recombinant DNA approach. The BBTV infected banana leaf samples were collected around Uttar Kannada district of Karnataka. Further, the total nucleic acid was extracted using dellaporta method. A 531bp PCR product containing coat protein coding region of BBTV was amplified using BBTVCPF and BBTVCPR primers and the amplified product was cloned into the pTZ57R/T and further sub-cloned in to the pQE30. After transformation in to JM 109 and M15 cells the clones were confirmed through PCR and sequencing. Amplification with expected size of 531bp and 100% homology with other isolates showed integrity of the clone. Further, the coat protein appeared to be expressed at 3hr after induction with 1mM IPTG. The expressed protein was analyzed through sodium-dodecyl sulphate-Poly acrylamide gel electrophoresis (SDS-PAGE). A band of 21kDa on the gel confirmed that coat protein was really fused to the His-tag. Further, 10mg/liter of the coat protein were purified using His-tag purification kit (Genei).