In most of the developing countries, lack of resources and little market accessibility are among the major factors that affect small farming household food security. This study aims to investigate the status of small farming households’ food security, and its determinants including the role of market accessibility factors in enhancing food security at household level. In addition, this study also determines the households’ perception about different kinds of livelihoods risks. This study is based on a household survey of 576 households conducted through face-to-face interviews using structured interviews in Punjab, Pakistan. Food security status is calculated using dietary intake method. The study findings show that one-fourth of the households are food insecure. The study findings reveal that farm households perceive increase in food prices, crop diseases, lack of irrigation water and increase in health expenses as major livelihood risks. Further, the results of logistic regression show that family size, monthly income, food prices, health expenses and debt are main factors influencing the food security status of rural households. Furthermore, the market accessibility factors (road distance and transportation cost) do significantly affect the small farming household food security. The results suggest that local food security can be enhanced by creating off-farm employment opportunities, improved transportation facilities and road infrastructure.

The agriculture sector is confronting numerous types of risk. Like other crops’ farmers, rice growers are also facing multifarious challenges, including production and environmental risk. On the one hand, they are suffering from water shortage. On the other hand, if a flood occurs, there is no proper water reservation management, especially in developing countries. Global warming is another erotic topic for agriculture sustainability. To address the growers’ perceptions and their attitudes toward such types of risks, a farm base survey was carried out, and rice farmers were investigated in Pakistan. Methods of equally likely certainty equivalent and risk matrix were applied to investigate the farmers’ risk perceptions and attitude. A probit model was also used to estimate the factors that influence farmers’ perceptions and attitudes toward risks. The outcomes of the study show that flood, input high prices, increasing temperature, and crop diseases are the perceiving risks among rice farmers. Different types of socio-economic factors are influencing farmers’ risk perceptions and their attitudes, such as age, education level, a distance of farms from rivers, and non-farm income. This study provides valuable information to the policymakers and concerning authorities to apprehend agricultural risks among rice farmers and farming sustainability, especially in developing countries.

Climate change is a serious threat to agriculture in many developing countries including Pakistan. Changing pattern of climate and its extreme conditions have already led to a decline in crop productivity. However, farmers in developing countries experience risks beyond just climate change, many of which are related to policy, strategy, and factor endowments. The impact of these risks have serious implications for food security, rural livelihood, farm households’ wellbeing, and, above all, their motive to adapt to these changes in the long-term. To have an in-depth knowledge of farmers’ perceptions about the changing climate, this study investigates various aspects such as the determinants of perception about various risk sources and the relevant mitigation and adaptation options. To do so, 480 farmers from agriculture-dominated Punjab province were randomly selected in order for us to evaluate their awareness levels, socioeconomic dynamics that influence their perceptions, and various factors that influence their perceptions to achieve the desired findings. We applied the principle factor analysis approach to ascertain major sources and strategies based on farmers’ perception and planned/practiced options. Further, regression analysis was done to evaluate the factors influencing the perception levels of farmers about risk sources. The results showed that majority of the farmers faced various risks, and were trying to adapt crop husbandry practices towards these perceived risks. Change in agricultural policies (3.96) was placed as the highest risk source, while the need for small dams/turbine schemes was the top priority for risk management strategy (mean value of 4.39). By observing the effect of farm and farmer’s characteristics on risk sources and risk management strategies, it was revealed that these characteristics ominously provoked farmers’ perspectives about risk sources and management strategies. The findings imply the need for coherent environmental policy that encompasses price stability, community-led adaptation campaigns, and easy/uninterrupted flows of information that enables the farming community to facilitate sustainable decision processes.

Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and user-friendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household’s food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households’ data. Furthermore, the objective of the study was to enhance food security awareness among individuals.

Semantic analysis is a particular technique, which is an interesting area of research that associates with Natural Language Processing (NLP), artificial intelligence, opinion mining, text clustering, and classification. Numerous text processing techniques are being used to find out sentiments from the comments, such as social media tweets, hoax, fiction, nonfiction, novels, books, movies, health care, and stock exchange. Agrarian experts’ opinions play a vital role in the agriculture sector that yields good crop productivity. This paper presents a descriptive analysis of agriculture experts’ opinions through machine learning methods based on textual data collection. The data has been collected by surveying various academia, research institute, and industry of Punjab, Pakistan. The impact of various agricultural inputs such as seed quality, soil quality, soil-intensive tillage, climate changes, water shortage, synthetic fertilizer, and precision technologies on crop productivity have been collected through questionnaires. This research provides a descriptive analysis of collected agrarians experts opinions to increase the crop yield by providing awareness regarding current agriculture inputs to farmers by using machine learning. The current research provides a cohesive expert guideline for improving crop productivity, useful for agricultural policymaking, and conveys adequate farmers’ knowledge. Consequently, the proposed method is an innovative way of discovering recommendations of agrarians through sentiment analysis in survey data using machine learning methods. Furthermore, to the best of our knowledge, agrarians experts opinions on enhancing crop productivity have been considered for the first time in Pakistan.

Zinc (Zn) is a fundamental micronutrient required by all living organisms. Zn deficiency among children under 5 years, pregnant, and child-bearing women has been identified in developing countries such as Pakistan. Biofortified crops can increase micronutrient levels and decrease deficiencies. Meanwhile, consumer acceptance is essential, given that genetic alterations can occur during biofortification, resulting in changes in sensory traits and the quality of grains. Therefore, the present study focuses on the determining factors for consumer acceptance of Zn-biofortified wheat., an experimental survey was conducted to achieve the study's objectives. Qualitative and quantitative data were collected and analyzed from 203 respondents in the Punjab province. The results regarding sensory perceptions revealed that people attached great importance to the appearance of the chapati prepared with Zn-biofortified wheat. Therefore, they were willing to purchase Zn-biofortified wheat when asked to choose between the conventional wheat and the Zn-biofortified wheat. Moreover, the probit model illustrates that the level of education in the family and having young children aged under 5 years in the household positively impacted the acceptance of Zn-biofortified wheat among the participants. The findings suggest that there is significant scope for promoting Zn-biofortified wheat in the country. It is also imperative to ensure its availability across various regions so that households with weak purchasing power can buy and address their Zn deficiency. Furthermore, policymakers could introduce reforms targeting business communities for food management, keeping Zn-biofortified wheat in the priority stream.