There is widespread interest in the impacts of climate change on agriculture in Sub-Saharan Africa (SSA), and on the most effective investments to assist adaptation to these changes, yet the scientific basis for estimating production risks and prioritizing investments has been quite limited. Here we show that by combining historical crop production and weather data into a panel analysis, a robust model of yield response to climate change emerges for several key African crops. By mid-century, the mean estimates of aggregate production changes in SSA under our preferred model specification are − 22, − 17, − 17, − 18, and − 8% for maize, sorghum, millet, groundnut, and cassava, respectively. In all cases except cassava, there is a 95% probability that damages exceed 7%, and a 5% probability that they exceed 27%. Moreover, countries with the highest average yields have the largest projected yield losses, suggesting that well-fertilized modern seed varieties are more susceptible to heat related losses.
Sustainable Development Goal 5 (SDG) on gender equality and women’s rights and at least 11 of the 17 SDGs require indicators related to gender dynamics. Despite the need for reliable indicators, stylized facts on women, agriculture, and the environment persist. This paper analyzes four gender myths: 1) 70% of the world’s poor are women; 2) Women produce 60 to 80% of the world’s food; 3) Women own 1% of the world’s land; and 4) Women are better stewards of the environment. After reviewing the conceptual and empirical literature, the paper presents the kernel of truth underlying each myth, questions its underlying assumptions and implications, and examines how it hinders us from developing effective food security policies.
Agri-Food is the largest manufacturing sector in the UK. It supports a food chain that generates over £108bn p.a., with 3.9m employees in a truly international industry and exports £20bn of UK manufactured goods. However, the global food chain is under pressure from population growth, climate change, political pressures affecting migration, population drift from rural to urban regions and the demographics of an aging global population. These challenges are recognised in the UK Industrial Strategy white paper and backed by significant investment via a Wave 2 Industrial Challenge Fund Investment ("Transforming Food Production: from Farm to Fork"). Robotics and Autonomous Systems (RAS) and associated digital technologies are now seen as enablers of this critical food chain transformation. To meet these challenges, this white paper reviews the state of the art in the application of RAS in Agri-Food production and explores research and innovation needs to ensure these technologies reach their full potential and deliver the necessary impacts in the Agri-Food sector.
Abstract Food sustainability transitions refer to transformation processes necessary to move towards sustainable food systems. Digitization is one of the most important ongoing transformation processes in global agriculture and food chains. The review paper explores the contribution of information and communication technologies (ICTs) to transition towards sustainability along the food chain (production, processing, distribution, consumption). A particular attention is devoted to precision agriculture as a food production model that integrates many ICTs. ICTs can contribute to agro-food sustainability transition by increasing resource productivity, reducing inefficiencies, decreasing management costs, and improving food chain coordination. The paper also explores some drawbacks of ICTs as well as the factors limiting their uptake in agriculture.
The digitization of agricultural advisory services in India requires robust Automatic Speech Recognition (ASR) systems capable of accurately transcribing domain-specific terminology in multiple Indian languages. This paper presents a benchmarking framework for evaluating ASR performance in agricultural contexts across Hindi, Telugu, and Odia languages. We introduce evaluation metrics including Agriculture Weighted Word Error Rate (AWWER) and domain-specific utility scoring to complement traditional metrics. Our evaluation of 10,934 audio recordings, each transcribed by up to 10 ASR models, reveals performance variations across languages and models, with Hindi achieving the best overall performance (WER: 16.2%) while Odia presents the greatest challenges (best WER: 35.1%, achieved only with speaker diarization). We characterize audio quality challenges inherent to real-world agricultural field recordings and demonstrate that speaker diarization with best-speaker selection can substantially reduce WER for multi-speaker recordings (upto 66% depending on the proportion of multi-speaker audio). We identify recurring error patterns in agricultural terminology and provide practical recommendations for improving ASR systems in low-resource agricultural domains. The study establishes baseline benchmarks for future agricultural ASR development.
Vipul Bundake, Veena Khilnani, Archana Kale
et al.
A pot experiment of maize was carried during summer seasons of March–July, 2023 and 2024 at the experimental field of Rashtriya Chemicals and Fertilizers, Mumbai, India, to assess the impact of multi nano micronutrients formulation (NM) on maize growth. The experiment was structured using a Completely Randomized Block Design with 12 treatments, including control with only water, Recommended Dose of Fertilizer (RDF), and different concentrations of NM having zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron (B) ranging from 20 mg to 0.15 mg 15 kg-1 of soil, as well as commercial micronutrients and micronutrient salts. Results revealed that application of 100% RDF+0.312 mg (T9) and 0.156 mg (T10) of nano micronutrients with drenching recorded better results of nutrient uptake (NU), apparent recovery (ANR) and agronomic efficiency (ARE). The NU (kg ha-1) of nitrogen (120.368), potassium (101.422), Cu (0.114), Fe (1.235), Mn (0.107) and Zn (6.069) was higher in T9 when compared to 100% RDF. The ANR was 9154.19% higher in T10 and 158.28% higher for Nitrogen(N), Phosphorus (P), and Potassium compared to 100% RDF. The protein and chlorophyll content were better in T9 and T10 of nano micronutrients respectively. The applications of T9 and T10 was found to be most effective in NU, ARE, ANR, protein content and chlorophyll content. Higher nutrient content in soil was found in treatment with lower concentrations. Overall, lower concentration of nano micronutrients appeared to be more effective for all traits.
Alavikunhu Panthakkan, S M Anzar, K. Sherin
et al.
Precision farming relies on accurate vegetation monitoring to enhance crop productivity and promote sustainable agricultural practices. This study presents a comprehensive evaluation of UAV-based imaging for vegetation health assessment in a palm tree cultivation region in Dubai. By comparing multispectral and RGB image data, we demonstrate that RGBbased vegetation indices offer performance comparable to more expensive multispectral indices, providing a cost-effective alternative for large-scale agricultural monitoring. Using UAVs equipped with multispectral sensors, indices such as NDVI and SAVI were computed to categorize vegetation into healthy, moderate, and stressed conditions. Simultaneously, RGB-based indices like VARI and MGRVI delivered similar results in vegetation classification and stress detection. Our findings highlight the practical benefits of integrating RGB imagery into precision farming, reducing operational costs while maintaining accuracy in plant health monitoring. This research underscores the potential of UAVbased RGB imaging as a powerful tool for precision agriculture, enabling broader adoption of data-driven decision-making in crop management. By leveraging the strengths of both multispectral and RGB imaging, this work advances the state of UAV applications in agriculture, paving the way for more efficient and scalable farming solutions.
Mateus Pinto da Silva, Sabrina P. L. P. Correa, Hugo N. Oliveira
et al.
Mapping agriculture in tropical areas through remote sensing presents unique challenges, including the lack of high-quality annotated data, the elevated costs of labeling, data variability, and regional generalisation. This paper advocates a Data-Centric Artificial Intelligence (DCAI) perspective and pipeline, emphasizing data quality and curation as key drivers for model robustness and scalability. It reviews and prioritizes techniques such as confident learning, core-set selection, data augmentation, and active learning. The paper highlights the readiness and suitability of 25 distinct strategies in large-scale agricultural mapping pipelines. The tropical context is of high interest, since high cloudiness, diverse crop calendars, and limited datasets limit traditional model-centric approaches. This tutorial outlines practical solutions as a data-centric approach for curating and training AI models better suited to the dynamic realities of tropical agriculture. Finally, we propose a practical pipeline using the 9 most mature and straightforward methods that can be applied to a large-scale tropical agricultural mapping project.