Food and Energy Security最新文献

This study examines the import demand for four key agricultural commodities—cereals, meat, sugar, and vegetable oils—in Türkiye, using a Linear Almost Ideal Demand System (AIDS) model. Spanning the period from 1986 to 2020, the analysis focuses on these commodities as they constitute over 90% of Türkiye's food import budget. The results reveal significant long-run own-price elasticities, with vegetable oils and cereals being particularly sensitive to price changes, whereas the impact of price on meat and sugar imports is negligible. The study also highlights the limited role of income and exchange rates on import demand, except for sugar where the exchange rate has a significant but small positive effect. Short-run estimates indicate a heightened responsiveness of budget allocations for cereals and vegetable oils to price fluctuations, suggesting a persistent element in food import patterns over time. The findings underscore the essential nature of these commodities, with low own-price elasticity for cereals and vegetable oils, reflecting their status as necessities. In contrast, short-run elasticity results suggest that cereal imports may be viewed as a luxury, with the potential for domestic production to substitute imports. The study concludes that Türkiye's food security is increasingly vulnerable to global price fluctuations, particularly for vegetable oils, and calls for policies that stabilize exchange rates and inflation while enhancing domestic agricultural productivity to mitigate this risk.

Among others, biotic and abiotic constraints associated with climate variability contribute to the low productivity of sorghum in Nigeria and other Sub-Saharan African countries. In this regard, improved sorghum varieties (ISVs) have been developed to address the constraints and boost the productivity of smallholder sorghum farmers. However, there is a scarcity of empirical studies on the adoption and impacts of ISVs. Using plot-level data from 3308 plots, we examine the drivers and impacts of the adoption of ISVs on the productivity and net income of sorghum farmers in Nigeria. To do so, we estimate an endogenous switching regression (ESR) model, which accounts for potential selection bias from observed and unobserved heterogeneity, and we perform some robustness checks. Our results show that the adoption rate of ISVs is about 25% in the study area. Among other factors, access to varietal information and distance to the seed market strongly explain the adoption of ISVs. The adoption of ISVs led to an increase in sorghum yield and net income by 13% and 17% respectively. Our results suggest that most smallholder sorghum farmers will not benefit from the productivity and income gains, given the relatively low adoption of ISVs. Overall, our findings imply that policymakers and development partners should increase investments in promoting the widespread adoption of ISVs through interventions, such as improved extension services and accessibility of seeds to deliver productivity gains to smallholder sorghum farmers.

The relationship between carbon and nitrogen metabolism and rice eating quality was examined in this study, which focused on four types of japonica rice from the middle and lower reaches of the Yangtze River. These japonica rice varieties were categorized by amylose content and protein content as follows: high amylose content with high protein content, high amylose content with low protein content, low amylose content with high protein content, and low amylose content with low protein content. The study systematically analyzed the carbon and nitrogen metabolism in the grain after flowering and assessed its impact on rice eating quality. Results showed that in japonica rice with similar protein content, low amylose content japonica rice had 5.15%–16.41% lower levels of granule-bound starch synthase (GBSS) compared to high amylose content japonica rice. Additionally, low amylose content japonica rice displayed 3.84%–23.80% higher levels of soluble starch synthase (SSS), 9.06%–31.05% higher levels of starch branching enzyme (SBE), and 20.27%–53.80% higher levels of starch debranching enzyme (DBE). The synthesis rate of amylose was lower in low amylose-content japonica rice, while the synthesis rate and content of albumin were higher. In japonica rice with similar amylose content, low protein content japonica rice had 3.41%–12.09% lower levels of nitrogen and 6.94%–20.15% lower levels of glutamate synthetase (GOGAT) compared to high protein content japonica rice. This resulted in a reduced protein synthesis rate and lower contents of glutelin, globulin, and prolamin. Moreover, low-protein content in japonica rice demonstrated higher levels of SBE and DBE, leading to a decrease in long-chain amylopectin. Correlation analysis revealed significant interactions between carbon and nitrogen metabolisms, which were closely linked to rice-eating quality. Carbon metabolism was the predominant factor, followed by nitrogen metabolism, in shaping the eating quality of rice. Carbon metabolism influenced rice-eating quality by modifying starch synthesis and interacting with nitrogen metabolic pathways, especially those involved in protein synthesis. Low GBSS and elevated SSS, SBE, and DBE levels in grains post-flowering could reduce amylose synthesis while promoting albumin synthesis, leading to improve the eating quality of japonica rice. Nitrogen metabolism further modifies the taste of cooked rice by adjusting protein synthesis and interacting with carbon metabolism, particularly in starch formation. Reduced nitrogen levels and GOGAT post-flowering could decrease protein synthesis, notably of glutelin and prolamin, and long-chain amylopectin in starch, thereby enhancing the eating quality of japonica rice.

Maize production contributes to food security and economic stability among smallholder farmers in the KwaZulu-Natal (KZN) province of South Africa, but it faces major challenges. Obstacles in smallholder maize production include limited water supplies, soil degradation, and biotic stressors such as pests and diseases. This study uses a case study methodology incorporating primary observations, secondary referencing, and literature reviews to examine the intricate relationship between smallholder maize production and agricultural/natural resources. The recommendations offered here focus on improving food security in KZN, including the integration of scientific and indigenous knowledge systems, farmer participation in decision-making processes, and the provision of educational programs tailored to smallholder farmers. Taken together, these recommendations can help strengthen smallholder participation in maize production in KZN.

Mercury (Hg) is a hazardous heavy metal that disrupts plant growth and metabolism, posing significant risks to agriculture and human health. The widespread use of Hg in industrial activities and agricultural chemicals has exacerbated Hg contamination in soil and water, resulting in severe phytotoxic effects on crops, including lettuce (Lactuca sativa L.). This study explores the potential of mannitol, an osmolyte known for its protective properties, to mitigate Hg-induced stress in lettuce. Lettuce plants were exposed to Hg stress (10 mg L⁻¹) and treated with foliar applications of mannitol (15 mM) to assess its impact on growth, photosynthetic performance, and oxidative stress responses. Under Hg stress, lettuce exhibited a significant reduction in relative growth rate (RGR) by 31% and relative water content (RWC) by 20%. However, mannitol treatment enhanced RGR by 18% and improved RWC by 13% in Hg-stressed plants. Nutrient analysis revealed that mannitol treatment restored K, Fe, Mn, and Zn levels, which were otherwise depleted by Hg exposure. Photosynthetic parameters were adversely affected by Hg, with a 62% reduction in carbon assimilation rate (A). Mannitol treatment partially recovered A by 55% and improved PSII photochemistry, including Fv/Fm and Fv/Fo. Mannitol also moderated oxidative stress indicators, evidenced by decreased H₂O₂ and lipid peroxidation levels, and enhanced the activities of key antioxidant enzymes such as superoxide dismutase (SOD), peroxidase (POX), and catalase (CAT). The results indicate that mannitol treatment significantly alleviates Hg-induced phytotoxicity in lettuce by improving growth, enhancing photosynthetic efficiency, and modulating oxidative stress responses. This study highlights the potential of mannitol as a viable strategy for mitigating Hg pollution effects, offering new insights into its application for improving crop resilience under heavy metal stress.

Biological breeding technology is a significant scientific and technological innovation in the world. It is also a crucial strategic plan in China's efforts to improve its seed industry. This paper examines the impact of transgenic technology on global crop export trade since its introduction in the production field. The study analyzed the binary marginal data of genetically modified (GM) soybean technological innovation and export in 41 countries worldwide from 2002 to 2012. The analysis utilized a panel regression model. The results indicate that GM technological innovation has a significant and positive association with the intensive margin of soybean export growth. However, it has an insignificant effect on the export extensive margin. The impact of GM technological innovation on the export extensive margin of different crops is heterogeneous. The cultivation of GM crops (GMOs) has been found to have a nonsignificant impact on food crops like soybeans and corn but a positive and significant impact on industrial commodity crops such as cotton. Furthermore, additional analyses indicate that the policy of planting GMOs has a positive moderating effect on technological innovation that affects the export-intensive margin while having a negative moderating effect on technological innovation that acts on the export-extensive margin. Technological innovation affects market forces and impacts the intensive margin of export growth. The global trade of crops has been driven by innovations in genetically modified technology. Policy support and market power have also played a significant role in the success of each country.

The pearl millet value chain in Niger, from sowing to the production of millet flour, is mainly done using manual labor. The objective of this study was to compare manual and motorized postharvest operations in pearl millet based on several criteria: output per hour, labor and energy demand, product quality, drudgery (measured by heart rate), profitability, farmers' perceptions of quality and cost of operations, and adoption rates. The study also assessed the conditions under which motorized operations could be adopted. The research methods included a survey of 200 households, interviews with entrepreneurs, and field measurements of both manual and motorized postharvest operations using the above criteria. An economic assessment was undertaken based on investment costs, time use, and the cost and income of each operation. The results showed that 72.5% of the households surveyed used motorized milling, whereas only 4% used motorized threshing. The time-saving effect of motorization was the highest for milling (211 h/Mg), whereas it was lowest for threshing (21 h/Mg). Motorized processing of 1 Mg of millet grain adequate for a household's yearly needs saves 49 man-days/year compared to manual methods. Quality measurements showed that only 38% of millet flour met quality standards after manual milling compared to 87% for motorized milling. By considering factors like fuel use and losses in the manual and motorized operations, it was found that motorized operations used 429 kWh/Mg less than the manual operation. Threshing is the most challenging postharvest operation to motorize, with investment costs four times higher than milling and dehulling, and lower profitability than other operations. Moreover, threshing and straw chopping are only done in the months following harvesting, whereas milling and dehulling services are in demand throughout the year. The study found that motorization of postharvest operations is an interesting option for farmers based on the criteria of time saving, quality of products, energy efficiency, reduction in women's workload, and profitability.

Plant phenomics deals with the measurement of plant phenotypes associated with genetic and environmental variation in controlled environment agriculture (CEA). Encompassing a spectrum from molecular biology to ecosystem-level studies, it employs high-throughput phenotyping (HTP) approaches to quickly evaluate characteristics and enhance the yields of crops in smart plant facilities. HTP uses environmental parameters for accuracy, such as software sensors, as well as hyperspectral imaging for pigment data, thermal imaging for water content, and fluorescence imaging for photosynthesis rates. They provide information on growth kinetics, physiological and biochemical characteristics, and genotype–environment interaction. Artificial intelligence (AI) and machine learning (ML) are used on a large volume of phenotypic data to predict growth rates, determine the optimal time to water plants, or detect diseases, nutrient deficiencies, or pests at an early stage. The lighting used in smart plant factories is adjusted based on the specific growth phase of the plants, such as using different light intensities, spectrums, and durations for germination, vegetative growth, and flowering stages, hydroponics as the method of providing nutrients, and CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) for improving certain characteristics, such as resistance to drought. These systems enhance crop production, yields, adaptability, and input use by optimizing the environment and utilizing precision breeding techniques. Plant phenomics with AI is a combination of several disciplines, promoting the understanding of plant–environment interactions in relation to agriculture problems such as resource use, diseases, and climate change. It affects their capacity to develop crops that capture inputs, minimize chemical application, and are resilient to climate change. Phenomics is cost-effective, reduces inputs, and contributes to more sustainable agricultural practices, being economically and environmentally sound. Altogether, plant phenomics is central to CEA due to its capacity to capitalize on phenotypic data and genetic potential within agriculture to advance sustainability and food security. Through phenomic research, the next advancements are likely to be even more revolutionary in terms of agricultural practices and food systems worldwide.

Drylands, encompassing 41% of global land and supporting over 2 billion people, face significant challenges, including water scarcity, extreme temperatures, and soil degradation. Dryland spans vast areas of Middle East and North Africa (MENA) and Sub-Sahara Africa (SSA) regions and poses a threat to food security and resilience. This study examines the potential of neglected and underutilized species (NUS) to improve dryland food and nutrition security, focusing on their agronomic performance, water productivity, economic viability, and nutritional benefits. Using long-term data from FAOSTAT, USDA Food Data Central, and peer-reviewed literature, we analyzed trends in the cultivation, yield, and nutritional contributions of 26 NUS across 22 countries in the MENA region comparing them with major staples—rice, wheat, and maize. Between 1961 and 2022, NUS crop areas in MENA fluctuated, decreasing by 7.0% since 2018 to 21.17 Mha. Despite this, NUS demonstrated superior water productivity—up to 30% higher than major cereals. For instance, sorghum and cowpea achieved 2.5 kg/m³ compared to maize (0.83 kg/m³) and wheat (0.91 kg/m³) and exhibited strong heat tolerance, withstanding temperatures of up to 42°C and 38°C, respectively. Despite a negative trade balance, NUS significantly contributed to dietary calories, surpassing wheat. A field experiment in Merchouch, Morocco, confirmed that NUS offered a higher economic value per unit than wheat, and outperformed conventional crops across key indicators. Integrating NUS into dryland farming systems can enhance food security, sustainability, and resilience to climate change. Advancing NUS requires breeding programs, tailored good agricultural practices, value addition and market linkage, supportive policies, and farmer education. Collaborative efforts among international organizations, governments, and civil society are crucial to mainstreaming NUS in agrifood systems and contributing to the diversity, sustainability, and resilience of dryland farming systems in MENA and SSA regions.

Implementation of context-specific solutions, including cultivation of varieties adapted to current and future climatic conditions, have been found to be effective in establishing resilient, climate-smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesizing that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world's largest banana gene bank in terms of base temperature (T_base) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. One hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype-specific T_base were modelled. Growth responses of two genotypes were validated under greenhouse conditions, and gas exchange capacity measurements were made. The model confirmed genotype-specific T_base, with 30% of the accessions showing a T_base below the reference of 14°C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T_base, although within subgroup diversity was revealed. Greenhouse validation further showed low temperature sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, with this case study on banana, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate-smart varieties.