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This study revisits the Great Canadian Grain Logistics Crisis of 2013-14 to explore the competitiveness of the country's grain exports. An approach to comprehending the dilemmas of the international grain supply chain and trade, and national logistics policy in an era of multinational corporations, draws upon the literature on global value chain analysis. This analysis identifies both the grain industry's global and local dimensions. An important literature on the 'politics’ of the supply chain is also called into play to discuss who controls what aspects. This task of interpreting the various steps in Canada's grain logistics chain recognizes the key economic actors - producers, grain companies, railway companies, port terminal operators and export buyers - and political struggles between them as they each seek to maximize their self-interest. Policy implications for streamlining logistics operations are drawn from identifying where changes in the supply chain arrangements have gained or lost opportunities in export markets, particularly in the Asia-Pacific region.

Conservation agriculture-based wheat production system (CAW) can serve as an ex ante measure to minimize loss due to climate risks, especially the extreme rainfall during the wheat production season in India. This study aims to examine whether farmers learn from their past experiences of exposure to climate extremes and use the knowledge to better adapt to future climate extremes.

The authors used data collected from 184 farmers from Haryana over three consecutive wheat seasons from 2013-2014 to 2015-2016 and multivariate logit model to analyse the driver of the adoption of CAW as an ex ante climate risk mitigating strategies based on their learning and censored Tobit model to analyse the intensity of adoption of CAW as an ex ante climate risk mitigation strategy. Farmer’s knowledge and key barriers to the adoption of CAW were determined through focus group discussions.

The analysis shows that the majority of farmers who had applied CAW in the year 2014-2015 (a year with untimely excess rainfall during the wheat season) have continued to practice CAW and have increased the proportion of land area allocated to it. Many farmers shifted from CTW to CAW in 2015-2016.

While farmers now consider CAW as an ex ante measure to climate risks, a technology knowledge gap exists, which limits its adoption. Therefore, designing appropriate methods to communicate scientific evidence is crucial.

This paper uses three years panel data from 184 farm households in Haryana, India, together with focus groups discussions with farmers and interviews with key informants to assess if farmers learn adaptation to climate change from past climate extremes.

The adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.

The probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.

The results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.

The major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.

By applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

Long-term forecasts about commodity market indicators play an important role in informing policy and investment decisions by governments and market participants. Our study examines whether the accuracy of the multi-step forecasts can be improved using deep learning methods.

We first formulate a supervised learning problem and set benchmarks for forecast accuracy using traditional econometric models. We then train a set of deep neural networks and measure their performance against the benchmark.

We find that while the United States Department of Agriculture (USDA) baseline projections perform better for shorter forecast horizons, the performance of the deep neural networks improves for longer horizons. The findings may inform future revisions of the forecasting process.

This study demonstrates an application of deep learning methods to multi-horizon forecasts of agri-cultural commodities, which is a departure from the current methods used in producing these types of forecasts.

This study aims to analyze the impact of global climate change on food security in the East African Community (EAC) region, using panel data analysis for five countries, over 2000-2014.

The determinants of food security are expressed as a function of rainfall, temperature, land area under cereal production, and population size. The paper used pooled fixed effects to estimate the relationship among these variables.

Findings show that food security in EAC is adversely affected by temperature. However, precipitation and increasing areas cultivated with cereal crops will be beneficial to ensure everyone's food security.

Actions for mitigating global warming are important for EAC to consolidate the region’s economic, political and social development/stability.

This study aims to explore the potentials of adopting salam-based crowdfunding for financing the agricultural sector in Afghanistan.

It adopts a triangulation approach, consisting of a number of methods used in gathering relevant information about Afghanistan’s agricultural sector, the concept of crowdfunding and salam (forward sale) contract. These methods include library research, reviewing documents provided by Afghanistan’s banks and semi-structured interviews with Islamic finance experts.

This study finds that salam-based crowdfunding is a viable Sharīʿah-compliant investment platform, which may offer some advantages for both potential investors and farmers or entrepreneurs.

The novelty of this paper lies in the proposed structure of salam-based crowdfunding.

Climate change has become one of the most important development challenges worldwide. It affects various sectors, with agriculture the most vulnerable. In Ethiopia, climate change impacts are exacerbated due to the economy’s heavy dependence on agriculture. The Ethiopian Government has started to implement its climate-resilient green economy (CRGE) strategy and reduce CO₂ emissions. Therefore, the purpose of this study is to examine the impact of CO₂ emission on agricultural productivity and household welfare.

This study aims to fill these significant research and knowledge gaps using a recursive dynamic computable general equilibrium model to investigate CO₂ emissions’ impact on agricultural performance and household welfare.

The results indicate that CO₂ emissions negatively affect agricultural productivity and household welfare. Compared to the baseline, real agricultural gross domestic product is projected to be 4.5% lower in the 2020s under a no-CRGE scenario. Specifically, CO₂ emissions lead to a decrease in the production of traded and non-traded crops, but not livestock. Emissions also worsen the welfare of all segments of households, where the most vulnerable groups are the rural-poor households.

The debate in the area is not derived from a rigorous analysis and holistic economy-wide approach. Therefore, the paper fills this gap and is original by value and examines these issues methodically.

The purpose of this paper is to propose a model that will demonstrate how the integration of Salam (exclusive agricultural commodity trade) with Takaful (micro-Takaful – a subdivision of Islamic insurance) and value chain can address major challenges facing the agricultural sector in Kano State, Nigeria.

The study conducted a thorough and critical analysis of relevant literature and existing models of financing agriculture in Nigeria to come up with the proposed model.

The findings indicate that measures undertaken to address the major challenges fail. In view of this, this study proposed Bay-Salam with Takaful and value chain model to solve a number of challenges such as poor access to financing, poor marketing and pricing, delay, collateral requirement and risk issues in order to avail farmers with easy access to finance and provide effective security to financial institutions.

The paper is limited to using secondary data. Therefore, empirical investigation can be carried out to strengthen the validation of the model.

The study outcome seeks to improve the productivity of the farmers through enhancing their access to finance. This will increase their level of production and provide more employment opportunities. In addition, it will boost financial inclusion, income generation, poverty alleviation, standard of living, food security and overall economic growth and development.

The novelty of this study lies in the integration of classical Bay-Salam with Takaful and value chain and create a unique model structure which the researchers do not come across in any research that presented it in Nigeria.

This paper aims at providing the evidence about how carbon sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO₂ rates through the adoption of appropriate cropping systems such as agroforestry.

Stratified randomly selected plots were used to collect data on tree diameter at breast height (DBH). Composite soil samples were collected from three soil depths for soil carbon analysis. Above ground biomass estimation was made using an allometric equation. The spectral signature of each plot was extracted to study the statistical relationship between carbon stock and selected vegetation indices.

There was a significant difference in vegetation and soil carbon stocks among the different land use/land cover types (P < 0.05). The potential carbon stock was highest in the vegetation found in sparsely cultivated land (13.13 ± 1.84 tons ha⁻¹) and in soil in bushland (19.21 ± 3.79 tons ha⁻¹). Carbon sequestration potential of the study area significantly increased (+127174.5 tons CO₂e) as a result of conversion of intensively cultivated agricultural lands to agroforestry systems. The amount of sequestered carbon was found to be dependent on species diversity, tree density and tree size. The vegetation indices had a better correlation with soil and total carbon.

The paper has addressed an important aspect in curbing greenhouse gases in integrated land systems. The paper brings a new empirical insight of carbon sequestration potentials of agroforestry systems with a focus on drylands.