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Background and Aims: Two main problems the globe currently facing are migration and weather variation. Weather change has a significant impact on the agricultural industry, which affects the majority of poor people. There is a dearth of adequate methodological documentation when examining the relationship between weather variation, agricultural output, and migration. We aimed to identify methodological reporting difficulties by reviewing the quantitative literature on weather‐related migration through agricultural channels. Methods: A systematic evaluation was conducted using papers published between January 2010 and June 2022, indexed in the SCOPUS, PUBMED, and Google Scholar databases. Using inclusion/exclusion criteria, we selected 22 original research articles out of 18,929 distinct articles for review, in accordance with the PRISMA guidelines. We extracted data from each study to understand how various concepts, research designs, and investigative techniques influence our understanding of migration patterns related to weather in the agricultural sector. Results: The majority (64%) of the study's data consisted of time series data. In 50% of the studies, secondary data were used. Additionally, 55% of these studies did not state the sample size. In 40% of the studies, model assumptions were fully adhered to, whereas in 36% of the studies, they were not followed at all. The majority of the articles used the Ordinary Least Squares technique, while about 41% applied the Two‐Stage Least Squares technique. Various tests were conducted across these studies, such as robustness checks (59.1%), endogeneity tests (31.8%), omitted variable bias tests (22.7%), sensitivity analyses (22.7%), and weak instrument tests (13.6%), to name a few. In the research we selected, the methodology section had various shortcomings and lacked organization. Furthermore, the justifications for deviations from model assumptions were unclear, potentially affecting the study outcomes. Conclusion: This study has important indications for researchers in studying climatic (weather) migration through agricultural channels besides for policymakers by giving a thorough review of the methods and techniques. Key points: Providing narrative insights on how various issues, research designs, and analytical techniques shape our understanding of the association between weather variation and migration via agricultural production.Providing a broad overview of the pertinent literature, covering 22 articles, as a complement to earlier studies.Our study has important ramifications for researchers studying climatic (weather) migration through agricultural channels as well as for policymakers by giving a thorough review of the methods and techniques currently used in this sector.Future studies on weather variation, agricultural production, and migration should use prudent and comparable models that capture whole climatic (weather) impacts on migration through mediating factors like agricultural production. [ABSTRACT FROM AUTHOR]

The main challenge facing agricultural crop production in the current global climate change scenario is sustainability. Drought, as a yield‐limiting factor, has become a major threat to international food security. Tolerance to drought is a complex trait and its response is carried out by various genes, transcription factors, microRNAs, hormones, proteins, cofactors, ions and metabolites. The complexity of the trait has limited the development of drought‐tolerant sweet potato cultivars by traditional breeding. Advances in sweet potato breeding to exploit the full potential of the crop to contribute to improved and higher performing sweet potato cultivars, adapted to increasingly risky rainfed conditions, mainly drought, are key to making food production systems more efficient and more tolerant to pressure from drought and other stressors. Genetic gain for yield potential in sweet potato has improved mostly in African countries, mainly as a result of an accelerated breeding scheme. The focus on maximising the utilisation of molecular tools for sweet potato improvement and yield has been recently explored in breeding programmes in sub‐Saharan Africa (SSA). This article provides an update on the trends and gaps in sweet potato breeding in SSA, reviewing the relevant strategies used to improve sweet potato in the region. Finally, the perspectives of using new advanced tools including genetic engineering and genome editing, and the challenges associated with climate change for further improvement of the crop are highlighted. Collaborative efforts in African countries are driving advances in breeding methods through the incorporation of molecular tools to develop drought‐tolerant sweet potato varieties that are important to global food security despite challenges posed by the drastic change in climate. [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally. Plain Language Summary: Northeast China (NEC) is essential for achieving food accessibility and food security in China, but its agricultural production is seriously threatened by climate change. This study investigated how climate change could impact maize, rice, and soybean crop yields in this region from 2015 to 2100, using the PCSE crop model and forcing from the international Coupled Model Intercomparison Project 6 (CMIP6) climate projections. Simulation results suggested rising temperatures would negatively affect crop yields, especially for maize. Increased carbon dioxide and rainfall partially offset these losses. By 2100, average declines were projected for maize (−21%) and soybeans (−4%), respectively. Altering planting dates to match crop needs was an effective adaptation to boost yields, with rice benefiting the most. Relocating rice production eastward and expanding its area substantially increased simulated rice yields (+62%) but decreased other crops due to competition (−32% maize, −28% soybeans). Even with adaptations, some areas still showed yield declines, indicating extra measures may be needed to maintain production. Overall, this study suggests significant risks to crop yields from climate change in Northeast China, with adaptations like optimized planting and strategic crop relocation able to mitigate some but not all of the projected impacts. Key Points: Rising temperatures negatively impacted simulated future crop yields, especially for maizeAdjusting planting dates boosted yields for rice and soybeans but had limited benefits for maizeRelocating and expanding the rice area increased yields substantially [ABSTRACT FROM AUTHOR]

Climate change is a constant threat to global food security. In Sub‐Saharan Africa (SSA), extended dry spells, heavy rainfall, flooding, and tropical storms have driven a large percentage of the population to a severe food crisis. Climate‐smart agriculture (CSA) is a potential comprehensive solution whose adoption can mitigate climate change and its ramifications on the state of food security in the region. CSA combines innovation, the use of technology, extensive research, stakeholder involvement, and data to increase food production, optimize resource allocation and utilization, and enhance the region's ability to feed its population. The adoption of CSA has succeeded in increasing agricultural productivity in several parts of SSA and will help towards the building of resilience food systems across the region amid constant and unpredictable climate change and its far‐reaching impacts in the region. For the sustained and universal adoption of CSA within the region, there is a need for more stakeholder involvement in its development and adaptation to the needs of specific communities. Furthermore, it is necessary to leverage the positive impact of the approach by expanding its scope beyond climate change mitigation to prevention and control, and ultimately help in achieving the United Nations SDG 2 and 13. [ABSTRACT FROM AUTHOR]

Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security. [ABSTRACT FROM AUTHOR]