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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]

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]

Access to credit has been a key component in protecting a country's agriculture sector against uncertainties and climate‐related shocks. Agricultural credits may also increase both agribusiness sectors' and farming‐related commercial activities' exposure to world markets. This study aims to investigate agricultural credits' short‐run and long‐run effects on agricultural production using control variables such as foreign direct investments, inflation rate, and government expenditures. We found that credits to agriculture affect value‐added agriculture positively in the long‐run; specifically, when agricultural credits increase by 1%, value‐added agriculture will increase by 0.19%; that is, an increase in credits to the agricultural sector leads to a significant increase in value‐added agriculture, while FDI and government size both reduce agricultural value‐added across countries. The findings of the pairwise causation test show that bidirectional causal links exist among almost all variables, validating feedback among agricultural value‐added, credit to agriculture, FDI, government expenditures, and inflation. [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]

Biofertilizers has potential to improve crop productivity in a sustainable manner yet their impact on forage crops yields and forage quality indices have not been extensively explored. In this study, the impact of different cropping patterns and fertilizer sources on the forage yield and quality of ajowan (Carum copticum L.), fenugreek (Trigonella foenum‐graecum L.), and pea (Pisum sativum L.) was investigated. The findings shed light on the importance of biofertilizer application as a sustainable solution for enhancing forage production and quality in agricultural systems. The experimental treatments involved six cropping patterns [ajowan monoculture, fenugreek monoculture, pea monoculture, intercropping of one row ajowan + one row fenugreek + one row pea (1:1:1), two rows ajowan + two rows fenugreek + two rows pea (2:2:2), and three rows ajowan + three rows fenugreek + three rows pea (3:3:3)] and five fertilizer sources [no fertilizer (control), 100% chemical fertilizer (CF), chemical fertilizer + humic acid (CF + HU), chemical fertilizer + biofertilizer (CF + BF), and humic acid + biofertilizer (HU + BF)]. The results revealed that the 2:2:2 intercrop fertilized with CF + BF had the highest N, P, and K concentrations for the three species. The monoculture systems with CF + BF produced the highest forage yields for ajowan (5492 kg/ha), fenugreek (3811 kg/ha), and pea (12,695 kg/ha). In addition, 2:2:2 intercrop fertilized with CF + BF produced the highest forage quality for all three species, decreasing the crude fiber, acid detergent fiber, and neutral detergent fiber contents and increasing forage ASH, dry matter intake, water‐soluble carbohydrates, and relative feed value. The 2:2:2 intercrop ratio combined with CF + BF application could be recommended to farmers as an eco‐friendly strategy (decreasing chemical fertilizer use) for improving forage productivity and quality in sustainable agricultural systems. [ABSTRACT FROM AUTHOR]

In recent years, China's crop production growth rate has decreased. This has provoked questions on whether the grain yields have reached their potential. Taking the North China Plain (NCP) as the study area, this study first simulated the potential yield of an irrigated maize crop with the WOFOST model, using daily weather data for 1960–2017. The yield gap was quantified for 1998–2017, and finally, the total potential maize production was estimated. The results indicate that during 1960–2017, the average potential yield for the region varied annually from 9.5 to 13.5 t ha−1, showing an obvious downward trend at a rate of 37.6 kg ha−1 per year. The long‐term mean potential yield at all 327 counties ranged from 9.9 to 13.7 t ha−1, increasing from south to north. The annual maize yield gap was between an average of 2.7–6.0 t ha−1 (29.0%–51.3%) of the potential yield for the NCP during 1998–2017. At county‐level, the yield gap varied between 1.9 and 7.8 t ha−1 and exceeded 5.0 t ha−1 for 99 counties (30.3% of all counties). Attainable yields can be increased by 0.1–5.2 t ha−1 at county‐level, and by 2.2 t ha−1 for the whole NCP, implying that the maize production has the potential to increase with 20.8 Mt. To increase maize production, the focus areas should be the southcentral NCP, which have a larger yield gap during 2012–2017. [ABSTRACT FROM AUTHOR]