N. O. Anyoha, Cornelia Udemba, A. Ogbonnaya, Emmanuel Okoroma
The study examined the causes of cassava post-harvest losses among farmers in Imo State, Nigeria. A structured questionnaire was used to obtain data from 120 rural farmers using multistage, random and purposive sampling techniques. Data were analysed using percentage, mean and standard deviation. Findings from the study reveal that farmers frequently used mobile phone calls/SMS as their sources of information on cassava postharvest losses (x̄ =3.01) followed by the use of radio( x̄=2.5) The findings also revealed that farmers experienced different forms of post-harvest cassava losses at one point or the other. Physical loss was experienced by 95.64% of the respondent, economic loss (93.8%) and monetary loss (97.7%).The causes of cassava post-harvest losses included lack of finance ( x̄=3.56) poor storage (x̄ =3.46) pest and disease infection (x̄ =3.46) inappropriate harvesting time (x̄ =3.40) among others. Most of the causes of cassava post-harvest losses identified in the study area are serious. Cassava farmers should be granted credit facilities and been courage to form cooperatives to help raise the funds and get facilities needed to reduce post-harvest losses.
{"title":"Causes Of Cassava Post-Harvest Losses Among Farmers In Imo State, Nigeria","authors":"N. O. Anyoha, Cornelia Udemba, A. Ogbonnaya, Emmanuel Okoroma","doi":"10.4314/jae.v27i2.7","DOIUrl":"https://doi.org/10.4314/jae.v27i2.7","url":null,"abstract":"The study examined the causes of cassava post-harvest losses among farmers in Imo State, Nigeria. A structured questionnaire was used to obtain data from 120 rural farmers using multistage, random and purposive sampling techniques. Data were analysed using percentage, mean and standard deviation. Findings from the study reveal that farmers frequently used mobile phone calls/SMS as their sources of information on cassava postharvest losses (x̄ =3.01) followed by the use of radio( x̄=2.5) The findings also revealed that farmers experienced different forms of post-harvest cassava losses at one point or the other. Physical loss was experienced by 95.64% of the respondent, economic loss (93.8%) and monetary loss (97.7%).The causes of cassava post-harvest losses included lack of finance ( x̄=3.56) poor storage (x̄ =3.46) pest and disease infection (x̄ =3.46) inappropriate harvesting time (x̄ =3.40) among others. Most of the causes of cassava post-harvest losses identified in the study area are serious. Cassava farmers should be granted credit facilities and been courage to form cooperatives to help raise the funds and get facilities needed to reduce post-harvest losses. ","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45578755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Akinwale, F. Wole-Alo, Babatunde Oladapo Oluwole
The study assessed digital platforms used to link agriculture investors with smallholder farmers in Nigeria. Content analysis and in-depth interviews were used for the study. Three digital platforms, namely the ThriveAgric, EZ Farming and Farmcrowdy were purposively chosen for this study. Results showed that ThriveAgric is user friendly; EZ Farming and Farmcrowdy provide risk assessment scores for easy monitoring. Also, ThriveAgric uses the Agricultural Operating System (AOS) to track farmers’ activities. EZ Farming uses social media platforms for adverts and campaign while Farmcrowdy innovatively used the platform to render advisory services to the farmers. ThriveAgric and Farmcrowdy invested in crop and livestock production while EZ Farming areas of investments were crop, livestock, fisheries, farm equipment and hydroponics. Smallholder farmers’ derived benefits were mainly observed in the aspect of reduction in post-harvest losses. Untimely delivery of agricultural inputs and cost of running the platforms were identified as major impediments to the effective deployment of the platform by the farmers and operators of the platforms respectively. Cost sharing mechanism should be incorporated into the partnership to ensure sustainability and a winwin situation. Also, regular review of the initiative by all the stakeholders is strongly recommended.
{"title":"Digital Platforms for Linking Agriculture Investors with Smallholder Farmers in Nigeria","authors":"J. Akinwale, F. Wole-Alo, Babatunde Oladapo Oluwole","doi":"10.4314/jae.v27i2.6","DOIUrl":"https://doi.org/10.4314/jae.v27i2.6","url":null,"abstract":"The study assessed digital platforms used to link agriculture investors with smallholder farmers in Nigeria. Content analysis and in-depth interviews were used for the study. Three digital platforms, namely the ThriveAgric, EZ Farming and Farmcrowdy were purposively chosen for this study. Results showed that ThriveAgric is user friendly; EZ Farming and Farmcrowdy provide risk assessment scores for easy monitoring. Also, ThriveAgric uses the Agricultural Operating System (AOS) to track farmers’ activities. EZ Farming uses social media platforms for adverts and campaign while Farmcrowdy innovatively used the platform to render advisory services to the farmers. ThriveAgric and Farmcrowdy invested in crop and livestock production while EZ Farming areas of investments were crop, livestock, fisheries, farm equipment and hydroponics. Smallholder farmers’ derived benefits were mainly observed in the aspect of reduction in post-harvest losses. Untimely delivery of agricultural inputs and cost of running the platforms were identified as major impediments to the effective deployment of the platform by the farmers and operators of the platforms respectively. Cost sharing mechanism should be incorporated into the partnership to ensure sustainability and a winwin situation. Also, regular review of the initiative by all the stakeholders is strongly recommended. ","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44999072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bhakta Bdr Shangshon, A. Polthanee, C. Wongsamun, P. Suriya
This study explored the impact of agricultural extension services on cereal production. Data were collected through a semi-structured questionnaire from a random sample of 262 farmers from four regions (east, west, north, and south) in Bhutan. Farmers assessed the impact of extension services on five aspects of cereal production (cereal seed, social, environmental, production, and marketing aspects). Percentages and an ordered logistic model were used to analyse the data. The study found a low level of farmers’ participation in extension services. The social aspect of cereal production was the most impacted by the extension programmes, while the marketing aspect was the least impacted. The farmers’ cultivated dry land (Coeff. = 0.21) and wetland (Coeff. = 0.72), their participation in extension services (Coeff. = 0.61), and the extra labour (Coeff. = 0.24) significantly contributed to cereal production. The provision of effective and high-quality extension programs by extension agents is critical for smallholder farmers to enhance their agricultural production.
{"title":"Impact of Agricultural Extension Services on Cereal Production among Rural Farmers in Bhutan","authors":"Bhakta Bdr Shangshon, A. Polthanee, C. Wongsamun, P. Suriya","doi":"10.4314/jae.v27i1.2","DOIUrl":"https://doi.org/10.4314/jae.v27i1.2","url":null,"abstract":"This study explored the impact of agricultural extension services on cereal production. Data were collected through a semi-structured questionnaire from a random sample of 262 farmers from four regions (east, west, north, and south) in Bhutan. Farmers assessed the impact of extension services on five aspects of cereal production (cereal seed, social, environmental, production, and marketing aspects). Percentages and an ordered logistic model were used to analyse the data. The study found a low level of farmers’ participation in extension services. The social aspect of cereal production was the most impacted by the extension programmes, while the marketing aspect was the least impacted. The farmers’ cultivated dry land (Coeff. = 0.21) and wetland (Coeff. = 0.72), their participation in extension services (Coeff. = 0.61), and the extra labour (Coeff. = 0.24) significantly contributed to cereal production. The provision of effective and high-quality extension programs by extension agents is critical for smallholder farmers to enhance their agricultural production.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47303856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study assessed the determinants of young farmers’ utilization of improved rice production practices in South West Nigeria. A multistage sampling procedure was used in the selection of 317 respondents for the study. Data were collected through the use of an interview schedule. Frequency counts, percentages, mean, and Probit- censored double-hurdle regression model was used to analyze the data. Findings reveal that the major improved rice production practices utilized by the young farmers included: the selection of improved rice varieties (88.3%) and appropriate use of agrochemicals (99.3%), while the most intensively utilized practices were: farm planning and use of cropping calendar (WMS=1.38), timely planting of rice (WMS=2.24) and safekeeping and handling of agro-chemicals (WMS=2.48). Age (β = -0.073646, -0.0039496). Years of education (β = -0.0565773, 0.003665), years of farming experiences (β = 0.0081413, 0.003140, availability of improved agricultural practices information (β = 0.0413969, 0.114309) and knowledge level of improved rice production practices (β = 0.0396945, 0.408986) had the likelihood of determining utilization and intensity of use of Improved rice production practices in the study area. Government and other relevant agricultural stakeholders should focus on the identified factors in view to enhance the utilization of improved rice production practices in the study area.
{"title":"Determinants of Youth Farmers’ Utilization of Improved Rice Production Practices in South West, Nigeria","authors":"T. Ogunkunle, O. Olaniyi, Likoetla Puseletso","doi":"10.4314/jae.v27i1.6","DOIUrl":"https://doi.org/10.4314/jae.v27i1.6","url":null,"abstract":"The study assessed the determinants of young farmers’ utilization of improved rice production practices in South West Nigeria. A multistage sampling procedure was used in the selection of 317 respondents for the study. Data were collected through the use of an interview schedule. Frequency counts, percentages, mean, and Probit- censored double-hurdle regression model was used to analyze the data. Findings reveal that the major improved rice production practices utilized by the young farmers included: the selection of improved rice varieties (88.3%) and appropriate use of agrochemicals (99.3%), while the most intensively utilized practices were: farm planning and use of cropping calendar (WMS=1.38), timely planting of rice (WMS=2.24) and safekeeping and handling of agro-chemicals (WMS=2.48). Age (β = -0.073646, -0.0039496). Years of education (β = -0.0565773, 0.003665), years of farming experiences (β = 0.0081413, 0.003140, availability of improved agricultural practices information (β = 0.0413969, 0.114309) and knowledge level of improved rice production practices (β = 0.0396945, 0.408986) had the likelihood of determining utilization and intensity of use of Improved rice production practices in the study area. Government and other relevant agricultural stakeholders should focus on the identified factors in view to enhance the utilization of improved rice production practices in the study area.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46194113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study evaluated the climate change impacts and adaptation strategies of cassava farmers in Ebonyi State, Nigeria. Multi-stage sampling was used to select 419 cassava farmers who administered the questionnaire. Data were collected using primary and secondary means and were analysed using mean, frequency, percentage, beta regression model, and ordinary least squares multiple regression techniques. Factors influencing cassava production are meteorological information (95.7%), tradition and culture (94.5%), improved technology utilization (93.1%) and low access to credits (71.8%). Climate change effects on cassava production were reduced biodiversity (95.2%), increased crop failure (97.6%), decreased yield (100%) and increased soil salinity (92.6%). Cassava farmers adapted to various practices such as planting improved cassava varieties (95.9%), insurance (3.3%), planting different crops (96.9%), and livelihood diversification (94.9%). Age, education, household size, farm size and extension contacts were significant determinants of climate change adaptation strategies of cassava farmers. Variables such as temperature, rainfall, humidity and sunshine had both positive and negative impacts on cassava production. The study recommends cassava farmers seek early warning signals and information on climate change before embarking on their farming operations to avert possible negative consequences.
{"title":"Climate Change Impacts and Adaptation Strategies of Cassava Farmers in Ebonyi State, Nigeria","authors":"Emeka Emmanuel Osuji, C. Igberi, N. Ehirim","doi":"10.4314/jae.v27i1.4","DOIUrl":"https://doi.org/10.4314/jae.v27i1.4","url":null,"abstract":"The study evaluated the climate change impacts and adaptation strategies of cassava farmers in Ebonyi State, Nigeria. Multi-stage sampling was used to select 419 cassava farmers who administered the questionnaire. Data were collected using primary and secondary means and were analysed using mean, frequency, percentage, beta regression model, and ordinary least squares multiple regression techniques. Factors influencing cassava production are meteorological information (95.7%), tradition and culture (94.5%), improved technology utilization (93.1%) and low access to credits (71.8%). Climate change effects on cassava production were reduced biodiversity (95.2%), increased crop failure (97.6%), decreased yield (100%) and increased soil salinity (92.6%). Cassava farmers adapted to various practices such as planting improved cassava varieties (95.9%), insurance (3.3%), planting different crops (96.9%), and livelihood diversification (94.9%). Age, education, household size, farm size and extension contacts were significant determinants of climate change adaptation strategies of cassava farmers. Variables such as temperature, rainfall, humidity and sunshine had both positive and negative impacts on cassava production. The study recommends cassava farmers seek early warning signals and information on climate change before embarking on their farming operations to avert possible negative consequences.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42158171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. A. Akinbile, Mubarak Akingbade, Abdulrahman O. Salaudeen
Contributions of Anchor Borrowers Programme to rice farmers’ productivity were investigated in Ekiti state. A simple random sampling technique was used to select 173 respondents and a structured questionnaire was used to obtain data on farmers’ level of productivity before and during ABP. Data were analyzed using descriptive statistics, PPMC, paired t-test at α0.05 and Linear regression. The average farm size cultivated for rice was 3.22±1.4 ha, and the majority (98.8%) used a combination of mechanical and manual methods to cultivate rice before and during ABP. Timely paddy off-take was 13.3% before and 62.4% during ABP. Land fragmentation and tenure system (M=1.91) and high cost of labour (M=1.86) were major constraints faced by the respondents. Farmers’ rice productivity increased from 1.67 to 2.65 under ABP. Incentives provided during ABP that contributed significantly to rice farmers’ productivity were rice seed use and timely use of herbicides and insecticides. The ABP contributed significantly to rice farmers’ productivity in Ekiti state. Problems associated with extension contact and quality of extension delivery would be effectively addressed with the deployment of e-agricultural extension to improve farmers' productivity.
{"title":"Contributions of Anchor Borrowers Programme to Rice Farmers’ Productivity in Ekiti State","authors":"L. A. Akinbile, Mubarak Akingbade, Abdulrahman O. Salaudeen","doi":"10.4314/jae.v27i1.5","DOIUrl":"https://doi.org/10.4314/jae.v27i1.5","url":null,"abstract":"Contributions of Anchor Borrowers Programme to rice farmers’ productivity were investigated in Ekiti state. A simple random sampling technique was used to select 173 respondents and a structured questionnaire was used to obtain data on farmers’ level of productivity before and during ABP. Data were analyzed using descriptive statistics, PPMC, paired t-test at α0.05 and Linear regression. The average farm size cultivated for rice was 3.22±1.4 ha, and the majority (98.8%) used a combination of mechanical and manual methods to cultivate rice before and during ABP. Timely paddy off-take was 13.3% before and 62.4% during ABP. Land fragmentation and tenure system (M=1.91) and high cost of labour (M=1.86) were major constraints faced by the respondents. Farmers’ rice productivity increased from 1.67 to 2.65 under ABP. Incentives provided during ABP that contributed significantly to rice farmers’ productivity were rice seed use and timely use of herbicides and insecticides. The ABP contributed significantly to rice farmers’ productivity in Ekiti state. Problems associated with extension contact and quality of extension delivery would be effectively addressed with the deployment of e-agricultural extension to improve farmers' productivity.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48985975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John Chukwuma Okoro, Desmond Emeka Ugah, Jacinta Amuche Aroh, Obinna Gorgio Obioha, C. Udoye, E. Agwu
The study assessed perceived factors influencing farmers’ preference for rice varieties grown in Enugu State, Nigeria. A structured interview schedule was used to obtain information from 150 rice farmers. Data collected were analysed using frequency counts and percentages. The major rice varieties grown by the farmers were Faro 44 (60.7%), R8 (28.7%) and Mass II (20.7%). The farmers’ major sources of information that perhaps influenced their preferences for rice varieties were fellow farmers (93.3%) and personal observation (86.7%).The farmers’ perceived factors influencing preference of varieties were high yield (74.7%) and cooking characteristics like not sticking together on cooking (64.0%), nice taste (60.0%) and rising better while cooking (59.4%). Aside from the preference for FARO 44 rice variety because of high yield, the perceived factors influencing farmers’ preference of other rice varieties (R8 and Mass) were mainly because of seed availability. Therefore, farmers need information on affordable sources of FARO 44 rice seeds for higher yield that could increase their profit and raise their standard of living.
{"title":"Perceived Factors Influencing Farmers’ Preference for Rice Varieties in Enugu State, Nigeria","authors":"John Chukwuma Okoro, Desmond Emeka Ugah, Jacinta Amuche Aroh, Obinna Gorgio Obioha, C. Udoye, E. Agwu","doi":"10.4314/jae.v27i1.8","DOIUrl":"https://doi.org/10.4314/jae.v27i1.8","url":null,"abstract":"The study assessed perceived factors influencing farmers’ preference for rice varieties grown in Enugu State, Nigeria. A structured interview schedule was used to obtain information from 150 rice farmers. Data collected were analysed using frequency counts and percentages. The major rice varieties grown by the farmers were Faro 44 (60.7%), R8 (28.7%) and Mass II (20.7%). The farmers’ major sources of information that perhaps influenced their preferences for rice varieties were fellow farmers (93.3%) and personal observation (86.7%).The farmers’ perceived factors influencing preference of varieties were high yield (74.7%) and cooking characteristics like not sticking together on cooking (64.0%), nice taste (60.0%) and rising better while cooking (59.4%). Aside from the preference for FARO 44 rice variety because of high yield, the perceived factors influencing farmers’ preference of other rice varieties (R8 and Mass) were mainly because of seed availability. Therefore, farmers need information on affordable sources of FARO 44 rice seeds for higher yield that could increase their profit and raise their standard of living.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44074574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. C. Oduehie, Godwin. E. Ifenkwe, H. Shu’aibu, Gold Adolphus
The study investigated the prevalence and consequences of insecurity on livelihoods of rural households in Enugu State, Nigeria. A three-stage random sampling technique was used to select a total of 108 respondents from nine communities for the study. Data were collected using a structured questionnaire. The data collected were analyzed with mean and percentages. The study revealed that looting of farm produce (54.6%), communal fights (76.9%), armed robbery (51.9%), rape (50.7%), kidnapping (58.3%) and cultism (65.7%) were the forms of insecurity prevalent in the study area. The result further reveals that poor road network (𝑥̅ = 3.3), corruption (𝑥̅ = 3.2), poor security system (𝑥̅ = 3.2), absence of basic infrastructural facilities (𝑥̅ = 3.2), feeling of marginalization (𝑥̅ = 3.1), grazing on arable cropland (𝑥̅ = 3.1), rural poverty (𝑥̅ = 3.1) and high level of rural unemployment (𝑥̅ = 3.1) were perceived to be immediate causes of rural insecurity Loss of livelihoods/income (𝑥̅ = 3.5), loss of lives (𝑥̅ = 3.4), disruption of supply and distribution of agricultural inputs and outputs (𝑥̅ = 3.4), destruction of crops (𝑥̅ = 3.3), displacement of households (𝑥̅ = 3.2), prevention of farming activities (𝑥̅ = 3.1) and decline in patronage of commercial riders (𝑥̅ = 3.1) were the major consequences of rural insecurity. Insecurity had negative consequences on the rural livelihoods in Enugu State, and thus, efforts should be made by security agencies in reduction or curbing occurrences of insecurities in the State.
{"title":"Prevalence and Consequences of Insecurity on Livelihood of Rural Households in Enugu State: Implication for e-Extension in Nigeria","authors":"T. C. Oduehie, Godwin. E. Ifenkwe, H. Shu’aibu, Gold Adolphus","doi":"10.4314/jae.v27i1.3","DOIUrl":"https://doi.org/10.4314/jae.v27i1.3","url":null,"abstract":"The study investigated the prevalence and consequences of insecurity on livelihoods of rural households in Enugu State, Nigeria. A three-stage random sampling technique was used to select a total of 108 respondents from nine communities for the study. Data were collected using a structured questionnaire. The data collected were analyzed with mean and percentages. The study revealed that looting of farm produce (54.6%), communal fights (76.9%), armed robbery (51.9%), rape (50.7%), kidnapping (58.3%) and cultism (65.7%) were the forms of insecurity prevalent in the study area. The result further reveals that poor road network (𝑥̅ = 3.3), corruption (𝑥̅ = 3.2), poor security system (𝑥̅ = 3.2), absence of basic infrastructural facilities (𝑥̅ = 3.2), feeling of marginalization (𝑥̅ = 3.1), grazing on arable cropland (𝑥̅ = 3.1), rural poverty (𝑥̅ = 3.1) and high level of rural unemployment (𝑥̅ = 3.1) were perceived to be immediate causes of rural insecurity Loss of livelihoods/income (𝑥̅ = 3.5), loss of lives (𝑥̅ = 3.4), disruption of supply and distribution of agricultural inputs and outputs (𝑥̅ = 3.4), destruction of crops (𝑥̅ = 3.3), displacement of households (𝑥̅ = 3.2), prevention of farming activities (𝑥̅ = 3.1) and decline in patronage of commercial riders (𝑥̅ = 3.1) were the major consequences of rural insecurity. Insecurity had negative consequences on the rural livelihoods in Enugu State, and thus, efforts should be made by security agencies in reduction or curbing occurrences of insecurities in the State. ","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43805147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Elvin Nyaboe Otara, H. Mogaka, S. N. Ndirangu, J. Mugwe
This study evaluated socioeconomic factors influencing the uptake of regenerative agriculture technologies in the dry lands of Embu County. Semi-structured questionnaires were administered to 400 farm households. Multivariate Probit model (MVP) and percentage were used to analyse the data. The findings of the study indicate that several socioeconomic factors including farming experience, farm size, main occupation, off-farm activities, age, gender, marital status and education level influenced the uptake of various regenerative agriculture technologies. Government and other inventors should take these factors into consideration while making decisions and formulating policies to support the dissemination and uptake of agricultural innovations.
{"title":"Socioeconomic Factors Influencing Uptake of Regenerative Agriculture Technologies in the Dry-lands of Embu County, Kenya","authors":"Elvin Nyaboe Otara, H. Mogaka, S. N. Ndirangu, J. Mugwe","doi":"10.4314/jae.v27i1.1","DOIUrl":"https://doi.org/10.4314/jae.v27i1.1","url":null,"abstract":"This study evaluated socioeconomic factors influencing the uptake of regenerative agriculture technologies in the dry lands of Embu County. Semi-structured questionnaires were administered to 400 farm households. Multivariate Probit model (MVP) and percentage were used to analyse the data. The findings of the study indicate that several socioeconomic factors including farming experience, farm size, main occupation, off-farm activities, age, gender, marital status and education level influenced the uptake of various regenerative agriculture technologies. Government and other inventors should take these factors into consideration while making decisions and formulating policies to support the dissemination and uptake of agricultural innovations.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43506119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.
{"title":"Automatic Irrigation Model Powered by Smart Rain Prediction Device in India","authors":"Mahadeo Ambildhuke Geeta, Gupta Banik Barnali","doi":"10.4314/jae.v27i1.9","DOIUrl":"https://doi.org/10.4314/jae.v27i1.9","url":null,"abstract":"This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.","PeriodicalId":43669,"journal":{"name":"Journal of Agricultural Extension","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45670412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：