Impact of Migration on Women Empowerment: A Situational Analysis of North-Bihar

The study was conducted during 2018-2019 to explore the impact of male migration innorth-Bihar in empowering women in different domain of household decisions. Five domainsnamely agricultural production, asset creation, health care, educational decision of childrenand leadership were considered and women empowerment indices were computed for eachdomain. The result revealed that migration of male member adequately empowered only29.44 per cent of women. Larger proportion of women respondents (43.89%) were foundunder moderately empowered category and 29.67 per cent were observed still under lowempowerment group. Women were adequately empowered in studied area to take decisionsrelated to health care, education and agricultural production. The decision to purchase andsale of assets still was under the jurisdiction of male counterpart as the patriarchal systemstill dominated. The role of women in decision making in all the farm activities, fromselection of crops to the sale of farm produce, showed comparatively more moderateempowerment indices. The paper concludes that the women are empowered to varyingextent in situation of male migration from their native places.

Modeling the Impact of Covid-19 on the Farm Produce Availability and Pricing in India

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

Consumer’s Behaviour in The Consumption of Fresh Farm Product During Covid-19 Pandemic at Alor Gajah, Melaka

The Malaysian Government implemented stringent containment measures to avoid the spread of COVID-19, including social isolation and the closure of businesses and schools. Although these steps are necessary to prevent the virus from spreading, many voices have raised concerns about their possible effects on the agri-food system. Therefore, this study aims to identify the effect of the Covid-19 pandemic on fresh farm consumption among consumers in Alor Gajah, Melaka. This study was guided by the following research objectives: (i) to investigate the impacts of Covid-19 pandemic on consumer attitudes and behaviours on food consumption at Alor Gajah. (ii) to find out the implications of the closure of Covid-19 on food security in Alor Gajah, Melaka. Besides, this study uses quantitative methods involving (n=154) residents in the district of Alor Gajah, Melaka. The research data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 20. Descriptive analysis was used, and the result shows that the food consumption behaviour changes during the Covid-19 pandemic especially on the fresh farm produce. The findings may provide information for the local government to develop a framework that will help to address the shortage of fresh agriculture products that were affected by the pandemic at Alor Gajah, Melaka. It will also help to prepare for an unexpected future crisis by building on existing emergency plans as well as long-term food-related strategies.

Characterization of women and youth smallholder agricultural entrepreneur’s in rural irrigation schemes in Vhembe district, South Africa

The purpose of the study was to characterize irrigated smallholder agricultural enterprises (ISAEs) in selected areas of Vhembe District, Limpopo Province. The characterization focused on the geophysical environment and on participants in ISAEs. Precipitation was at most 460mmpa for villages along Madimbo Corridor and 701-1380mmpa for those along Mutale Valley, and temperatures were 38.1℃-44.0℃ (Madimbo) and 30.0℃-40.0℃ (Mutale). Groundwater supplemented surface water and was utilized more at Madimbo Corridor compared to Mutale Valley. The study area was characterized as semi-arid to sub-humid, hence technologies for efficient irrigation should be promoted. Participants in ISAEs were female (94.9%), and adult (52.72%) with low education levels (67.7% ≤ secondary education). The majority (88.65%) were not formally employed (54.61% self-employed, 34.04% full-time farmers). Participants experienced some level of poverty, 68.03 percent received low household incomes (R1001-R5000/month), 77 percent received social grants. Interestingly, the majority (65.31%) stayed in multiple-roomed houses, had cement brick walls, and corrugated iron roofs (54.42%), and all had electricity, a stove, and a fridge. Also, majority-owned radio (96.67%), DSTV (87.45%), vehicles (65.56%), and cellphones. Participants mostly provided adequate food supply (91.84%) with three meals/day (79.38%) except during hard times where 49.56 percent provided fewer meals mostly due to delayed readiness of farm produce. Strategies to empower ISAE participants to be more effective should consider their gender, age, education, and economic status estimated by income, asset ownership, and food security.

Profitability of various cropping patterns among arable crop farmers in Niger State, Nigeria

Descriptive Statistics and Net Farm Income model was used to analyze data collected from 120 Arable Farmers who adopted various cropping patterns in Niger State, Nigeria. The study specifically examined the socio-economic characteristics of arable farmers, profile the cropping patterns adopted, examined the profitability and highlighted the constraints to crop production among arable farmers in the study area. Results obtained from the study show that crop farming in the area is a male dominated. The mean age of farmers was 33years, 98.3% were married, 80.8% had one form of education or the other and 68.4% adopted a three-crop mix pattern in their crop production. Two and three crop mixes enterprise were profitable than sole cropping when gross income per ha was used as an index of profitability. Profitability was higher in single crop enterprise when returns/man day was used as an index but was higher in a two and three crop mix enterprise when net returns per ha was used as a measure of profitability. Bad roads, drought, theft of farm produce, poor extension per farm advisory services and lack of credit facilities respectively were the constraint to crop production. The study concludes that mixed cropping enterprises was more profitable than sole cropping. We recommend the promotion of mixed cropping among arable farmers for increased profitability and income to farm households, that the constraints identified be addressed by all concerned authorities so as to sustain crop production, reduce food insecurity and eradicate hunger and poverty among arable farmers in the area and Nigeria as a whole.

Agri-input Buying Behaviour of Paddy Farmers: A Study in the Context of the New Normal Due to COVID-19

Humanity has witnessed diseases and illnesses since the ancient days. These diseases and illnesses resulted in days of suffering leading to disabilities or deaths of many people within communities and are termed as epidemics. As the society progressed, widespread trade increased the interactions between human, animals and ecosystems, thereby increasing the occurrence of these epidemics, often named as pandemics. Several pandemics have afflicted the world throughout history, be it malaria, tuberculosis, leprosy, influenza, smallpox, plagues or HIV/AIDS or the recent incidence of Novel Coronavirus. Diseases affect the supply chain of farm produce and agriculture. Consequently, it may impact food security. It is assumed that pandemic affects the buying behaviour of farmers and it has the capacity to alter the buying behaviour of paddy farmers in India. In this study, an attempt is made to investigate the effect of certain factors on farmers purchase behaviour during pandemic situation among rice farmers in Chhattisgarh, India. The studied sample included 120 farmers in Dhamtari, Raipur, India, selected randomly. Narratives were collected from the farmers and were analysed using qualitative data analysis software. From the qualitative data analysis, the implication for marketers is that itinerant trainers should be sent to villages to train the farmers, especially the bigger farmers (who have secondary influence), on the new technologies in agricultural inputs. The farmers also influence each other, and some amount of training coverage may eventually reach all farmers. The local dealers and the village headmen should also be influenced. An attempt should be made to marry the new technologies with the traditional methods, as much as possible.

Conceptual design of smart multi-farm produce dehydrator using a low-cost programmable logic controller and raspberry pi

Background: Acceptable food processing techniques require the removal of water contents from the crop or food sample without destroying the nutritional qualities of the food sample. This poses a strict requirement on the dehydrator or oven that will be used in the dehydrating techniques to have the ability to control both temperature and humidity of its drying chamber. Methods: This work centres on how an autonomous multi-farm produce dehydrator that can also serve as an oven can be designed with a raspberry pi and a low-cost programmable logic controller (PLC). The dehydrator gives the users the flexibility to control both the drying chamber’s temperature and humidity from its web interface via a mobile device or the dehydrator’s HMI. Heat energy from the Liquid Petroleum Gas (LPG) is used so that the dehydrator can be readily available for commercial or industrial use. The small electricity required to power the electronics devices is obtained from the hybrid power solution with an electric energy source from either the mains electricity supply or solar.. The design was tested by creating an operation profile from the proposed web application for the dehydrator. The operation trend was analysed from the web application’s Trendlines page. Results: The report showed that both the temperature and humidity of the dehydrator could be controlled, and access to historical operation data will give insight to the user on how to create a better operation profile. Conclusion: The setup described in this work, when implemented was able to produce a dehydrator/oven whose temperature and humidity can be perfectly controlled and its generated heat is evenly distributed in its drying chamber to ensure efficient and effective drying techniques use in crop preservation and food processing.

Agro Defence: Engineering for Agriculture

Today’s world is unimaginable without technology, which plays a key role in all fields of society. We see numerous technological developments in all kinds of fields like automobiles, communication, Artificial Intelligence, etc. So, we have come up with something which can help the agricultural sector. The main aim of this application is to create an interface between farmers and farm produce consumers and it connects farmers across India with the end buyers eliminating the middlemen. To help the farmers, the buyers can pre-book for a crop so that farmers don’t need to take a loan. Without added expense to promote and distribute products farmers can earn better prices for their products and Seasonal crops like mango, rabi, peas, barley, etc. may drive to large market potential like paddy, wheat, etc. will regulate sales in local areas too. The farmers can achieve huge market potential through the transport of these seasonal crops. Mainly this application works throughout the process i.e., pre-processing, processing and post-processing of the crop and interfacing farmers with technological aid to address the labor shortage, product knowledge on chemical inputs and market linkages. This is how we imagined modern technological advancements in the development of the most underrated yet the most important sector i.e. Agriculture. This way the farmers can not only make additional income but also can live with contentment.