Since blockchain technology has proven to be effective in the development of a wide range of industries, its use in other fields is also being expanded. Agriculture is one such sector, where blockchain technology is being used to improve farm business operations. Today, several agribusiness firms are utilizing technology to improve food supply chain tracking. For example, Farmers Edge, the world’s leading company that revolutionized the field of digital agriculture through its work in providing advanced artificial intelligence solutions, as well as new opportunities that give agriculture a globally advanced future for all stakeholders, has taken a significant step forward. The issue of blockchain network technology and its applications in agriculture will be discussed in this study, as well as the key advantages that this technology can provide, when employed to make the lives of both producers and consumers easier. In addition, a total of 79 research papers were evaluated, with a focus on the state of blockchain technology in agriculture, related issues, and its future importance, as well as relevant contributions to this new technology and the distributions of this study by different countries.

In Nigeria, the high dependence on fossil fuels for energy generation and utilization in various sectors of the economy has resulted in the emission of a large quantity of carbon dioxide (CO${}_2$), which is one of the criteria gaseous pollutants that is frequently encountered in the environment. The high quantity of CO${}_2$ has adverse implications on human health and serious damaging effects on the environment. In this study, multi-decade (1971–2014) CO${}_2$-emissions data for Nigeria were obtained from the World Development Indicator (WDI). The data were disaggregated into various emission sources: gaseous fuel consumption (GFC), liquid fuel consumption (LFC), solid fuel consumption (SFC), transport (TRA), electricity and heat production (EHP), residential buildings and commercial and public services (RSCPS), manufacturing industries and construction (MINC), and other sectors excluding residential buildings and commercial and public services (OSEC). The analysis was conducted for a sectorial trend using a rank-based non-parametric modified Mann–Kendall (MK) statistical approach and a change point detection method. The results showed that the CO${}_2$ emissions from TRA were significantly high, followed by LFC. The GFC, LFC, EHP, and OSEC had a positive Sen’s slope, while SFC, TRA, and MINC had a negative Sen’s slope. The trend analysis indicated multiple changes for TRA and OSEC, while other sources had a change point at a particular year. These results are useful for knowledge of CO${}_2$-emission sources in Nigeria and for future understanding of the trend of its emission for proper environmental planning. The severe effects of CO${}_2$ on the atmospheric environment of Nigeria may be worsened in the future due to some major sources such as transportation services and electricity generation that are inevitable for enviable standard of living in an urban setting.

In this study, we propose a robust approach to handling geo-referenced data and discuss its statistical analysis. The linear regression model has been found inappropriate in this type of study. This motivates us to redefine its error structure to incorporate the spatial components inherent in the data into the model. Therefore, four spatial models emanated from the re-definition of the error structure. We fitted the spatial and the non-spatial linear model to the precipitation data and compared their results. All the spatial models outperformed the non-spatial model. The Spatial Autoregressive with additional autoregressive error structure (SARAR) model is the most adequate among the spatial models. Furthermore, we identified the hot and cold spot locations of precipitation and their spatial distribution in the study area.

Floods in Germany have become increasingly frequent and severe over recent decades, with notable events in 2002, 2013, and 2021. This study examines the trends and drivers of flood occurrences in Germany from 1990 to 2024, focusing on the influence of climate-change-related variables, such as temperature, precipitation, and river discharge. Using a comprehensive time series analysis, including Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) models and correlation and regression analyses, we identify significant correlations between these climatic variables and flood events. Our findings indicate that rising temperatures (with a mean of 8.46 °C and a maximum of 9 °C) and increased precipitation (averaging 862.26 mm annually)are strongly associated with higher river discharge (mean 214.6 m³/s) and more frequent floods (mean 197.94 events per year). The ANN model outperformed the ARIMA model in flood forecasting, showing lower error metrics (e.g., RMSE of 10.86 vs. 18.83). The analysis underscores the critical impact of climate change on flood risks, highlighting the necessity of adaptive flood-management strategies that incorporate the latest climatic and socio-economic data. This research contributes to the understanding of flood dynamics in Germany and provides valuable insights into future flood risks. Combining flood management with groundwater recharge could effectively lower flood risks and enhance water resources’ mitigation and management.

Irrigation remains a critical input into farming and, therefore, it remains a proxy for food security and poverty alleviation in developing countries. Despite their role in economic growth and the received investment, irrigation schemes are still underperforming. Among many irrigation schemes, transferring more management responsibilities to farmers proved to be a viable path toward improving performance. However, the large diversity of implementation strategies makes evaluation difficult, while its well-demonstrated benefits have paramount importance to convince reluctant smallholders to take additional roles. In order to address this gap, we analyze the effects of participation on farming outcomes (yield, revenue, net profit) by estimating the treatment effect. We present the case study of a Mubuku small-scale irrigation scheme, Uganda. We provide a framework to construct the Farmers Participation Index while distinguishing farmers into participating and not participating groups. The effects of participation are investigated through econometric methods including nonparametric and semiparametric estimation methods such as a difference in means, a regression adjustment, propensity score matching, and entropy balancing. The analysis reveals a positive and significant treatment effect of participation on farming outcomes. The obtained results endorse the efforts of governmental programs to foster responsibility transfer and the farmers’ role in irrigation management. A strong causal relationship between management and profitability provides incentives for farmers to engage in participation.

Nowadays, even with the growth and progress of the agricultural sector, the food gap (FG) is still wide, particularly for strategic crops, affecting the national economy and compromising the food security. The realization of self-sufficiency can be fulfilled only by achieving the highest production efficiency along with preserving the natural resources currently available, especially arable land and irrigation water. In this analysis, the FG in Egypt was modeled for 13 crops between the years 2000 and 2018. The linear model applied suggested a redistribution of crops in terms of production, food demand and land reallocation, in order to find the best solution to minimize the FG on the basis of crop value and under a set of constraints. It was found that the value of the modelled FG increased steadily from 2005 to 2017, then it started to decline slightly, probably due to the steady increase in the population growth rate which is a crucial factor in enlarging the FG. Furthermore, important water loss was noticed through the analysis period. In fact, there was a huge difference, reaching around 25 billion m³ between the water consumed for the studied crops and the total amount of renewable water. The main reason for this loss can be linked to the traditional irrigation methods used, such as surface irrigation. Moreover, the calculation of food demand with the estimated production and the redistribution of crop land reallocations were performed to achieve the best model fit between the crops in terms of minimizing the FG in Egypt. So far, the current agricultural policy has reaped limited gains and a steep decline of food economic balance. Hence, significant interest on rising productivity should be given by the government to achieve the food self-sufficiency in Egypt.