Search Results (776)

Rice production in Sub-Saharan Africa (SSA) faces significant challenges due to insect pest infestations, which threaten food security and farmer livelihoods. This review examines the major insect pests affecting rice in SSA and highlights sustainable management strategies, drawing on successful case studies. It explores successful methods, including the use of biological control agents in Nigeria; neem-based pesticides in Tanzania; push-pull technology in Kenya; agroecological practices in Mali; resistant rice varieties in Ghana and Nigeria; integrated farming systems in Liberia, Guinea Conakry, Nigeria, Kenya and Madagascar; and farmer field schools in Zambia. Emerging technologies such as biotechnology and precision agriculture offer further additional opportunities to enhance pest control when effectively integrated within existing IPM frameworks. However, financial constraints, limited awareness, policy-related challenges, and inadequate infrastructure continue to limit widespread adoption. In this context, the review identifies critical research gaps, including the need for region-specific solutions, improved biopesticides, and long-term assessment of sustainable practices. Policy recommendations call for greater government investments, capacity-building programs, supportive regulatory environments, and stronger collaboration among researchers, development partners, and local stakeholders. Addressing these challenges can foster resilient and sustainable rice production systems across SSA. Full article

The three northeastern provinces of China are the country’s most important grain-producing region, particularly for maize, soybean, and rice, and form its largest commercial grain base. Over the past two decades, cropping structures in this region have undergone notable shifts driven by both climate change and human activities. Generating long-term, high-resolution maps of multi-crop distribution and evaluating their suitability is essential for understanding cropping dynamics, optimizing land use, and promoting sustainable agriculture. In this study, we integrated multi-source satellite imagery from Landsat and Sentinel-2 to map the distribution of rice, maize, and soybean from 2000 to 2023 using a Random Forest classifier. A crop suitability assessment framework was developed by combining a multi-criteria evaluation model with the MaxEnt model. Reliable training samples were derived by overlaying suitability evaluation results with stable crop growth areas, and environmental variables—including climate, topography, soil, hydrology, and anthropogenic factors—were incorporated into MaxEnt to assess suitability. Furthermore, the spatial consistency between actual cultivation and suitability was evaluated to identify areas of misallocated land use. The results show that: (1) the six classification maps achieved an average overall accuracy of 91.05% and a Kappa coefficient of 0.857; (2) the cultivation area of all three crops expanded, with maize showing the largest increase, followed by soybean and rice, and the dominant conversion being from soybean to maize; (3) suitability areas ranked as soybean (376,692 km²) > maize (329,056 km²) > rice (311,869 km²), with substantial spatial overlap, particularly between maize and soybean, suggesting strong competition; and (4) in 2023, highly suitable zones accounted for 57.39% of rice, 39.69% of maize, and 28.89% of soybean cultivation, indicating a closer alignment between actual distribution and suitability for rice, weaker for maize, and weakest for soybean, whose suitable zones were often displaced by rice and maize. These findings provide insights to guide farmers in optimizing crop allocation and offer a scientific basis for policymakers in designing cultivated land protection strategies in Northeast China. Full article

The development of diverse forms of agricultural scale operations is widely recognized as a cornerstone of modern agricultural management. Most existing studies largely examine land-scale or service-scale operations in isolation and pay little attention to their potential synergies in achieving economies of scale. Using survey data on 1026 plots from 865 rice farmers in Jiangsu Province, China, this study employs fixed-effects regression models to investigate how land-scale and service-scale operations jointly promote scale economies through agricultural machinery utilization. The empirical results reveal three key findings: (i) both land-scale and service-scale operations significantly reduce per-mu (1 mu = 0.067 ha) machinery costs, thereby generating scale economies; (ii) their synergy further amplifies these economies, providing strong evidence of synergy rather than substitution; and (iii) village governance significantly moderates this relationship, with stronger governance reinforcing the synergistic effects between land- and service-scale operations. These findings suggest that dual agricultural scale operations are mutually reinforcing in promoting mechanization. Policy should therefore prioritize their synergistic development and recognize the coordinating role of village collectives. Full article

In Nigeria’s northwestern states of Kano, Katsina, and Kaduna, mechanized rice production is an important contributor to household income and rural economic activity, especially amid a rapidly growing population projected to exceed 400 million by 2050. This study investigates the socio-economic insights of mechanized rice farmers and assesses the impact of mechanization on income, seasonal production, government support, and rural poverty alleviation. Data were collected from 125 respondents across 14 local government areas by using structured questionnaires and analyzed through descriptive statistics and hybrid machine learning models. The findings show that revenue generation significantly influences the adoption of mechanized rice farming, while government involvement is limited and largely ineffective. Advanced predictive modeling revealed that hybrid approaches, particularly those combining regression and Artificial Neural Networks with Bayesian Optimization, outperformed traditional models in forecasting rice yield. Key challenges identified include the high cost of equipment and restricted access to subsidized inputs. This study concludes that income from rice sales drives mechanization and that targeted policy interventions are necessary to overcome socio-economic barriers and improve productivity. These findings highlight the dual importance of economic empowerment and technological innovation in advancing sustainable rice production and improving livelihoods in Nigeria’s rice-growing regions. Full article

Methanotrophs offer promising avenues for sustainable agriculture and climate mitigation. This study evaluates the efficacy of indigenously isolated methanotrophs, particularly Methylomonas Kb3, as bioinoculants in rice cultivation. Kb3-treated plants exhibited early flowering, increased height, and a grain yield up to 17% higher than that of untreated controls. A mixed inoculation of Methylomonas and Methylomagnum resulted in a 15% increase in yield, indicating limited synergistic benefit. The root-dipping method during transplantation proved to be a practical and scalable inoculation technique for farmers. Genomic analysis revealed that Methylomonas Kb3 harbours genes associated with nitrogen fixation and resistance to heavy metals and antibiotics, potentially underpinning its agronomic performance. Beyond yield enhancement, the application of methanotrophs may contribute to reduced methane emissions in flooded paddy systems, offering dual benefits for both productivity and environmental sustainability. These findings warrant multilocation trials to validate efficacy across diverse agro-climatic zones and support the development of climate-smart biofertilizer strategies. Full article

Rice farming is a double-edged sword essential to humans as a staple food, yet it is also a source of greenhouse gases that contribute to climate change, a threat to human life. Adopting innovative technologies is one of the sustainable ways to maintain rice production and mitigate climate change. This review aims to comprehensively explore and analyze innovative climate-resilient technologies on productivity, environment, and economic sustainability to implement net-zero agriculture. We conducted a bibliometric analysis based on Scopus data using RStudio and VOSViewer and a systematic literature review using PRISMA guidelines with keywords such as rice, agriculture, technology, land, sustainable, economy, profitability, environment, and ecology. A total of 703 articles were obtained in the initial stage, and 27 articles were deemed eligible for further analysis. We found that precision agriculture, biofertilizers, climate-resilient rice varieties, irrigation technologies, carbon and methane mitigation strategies, and mechanization technologies can increase productivity and mitigate climate change. Adopting these innovative technologies also has a positive impact on environmental and economic sustainability, as well as farmers’ livelihoods. This review emphasizes the importance of collaboration among scientists, the private sector, farmers, and policymakers to achieve food security amidst climate change. Full article

The use of digital technologies can break down information barriers in rural areas, thereby creating crucial conditions for the widespread adoption of green agricultural technologies (GATs) among farmers. To explore the relationship between digital technology use (DTU) and farmers’ adoption of GATs, this study draws on 18 in-depth interviews and 608 survey responses collected from rice farmers in Sichuan Province, China. By adopting a mixed-methods design, it offers a comprehensive examination of the mechanisms through which digital technology use (DTU) promotes the adoption of green agricultural technologies (GATs) among farmers. Grounded theory analysis reveals that the DTU–GATs adoption pathway can be conceptualized within a “condition–process–outcome” framework. Specifically, digital infrastructure, farmers’ capital endowment, and practical needs constitute the foundational conditions, while technology perception and the regional soft environment act as key mediating processes. The ultimate outcomes include improvements in economic performance, social well-being, and ecological sustainability. Empirical evidence confirms that DTU significantly promotes the adoption of GATs, primarily by enhancing farmers’ perceptions of technology and improving the agricultural soft environment at the regional level. Moreover, the effects of DTU display substantial heterogeneity across different types of green technologies and among various farmer groups. These findings highlight the importance of strengthening digital infrastructure in rural areas, enhancing farmers’ digital literacy and capacity, and leveraging digital tools to tailor the dissemination and guidance of GATs. Such efforts are essential to raise farmers’ awareness, foster a supportive soft environment for sustainable agriculture, and ultimately advance the adoption of GATs. Full article

Organic rice farming (ORF) can support both climate change mitigation and adaptation. However, a deeper understanding of its specific benefits and challenges is needed. This paper synthesises current knowledge on the potential of ORF to enhance resilience in regions exposed to natural hazards, with particular attention to the climate-vulnerable region of the Mekong Delta (MKD), Vietnam. ORF can deliver multiple benefits: reducing production costs, revitalising degraded and pesticide-contaminated soils, improving water and soil quality, enhancing biodiversity, and contributing to human health and sustainable livelihoods. In the context of MKD, where rice production intersects with acute vulnerability to salinity intrusion, storms, and drought, ORF also presents opportunities for long-term adaptation by improving ecosystem health and reducing socio-ecological vulnerability. Despite these benefits, ORF remains limited in scale and impact due to the lack of integrated, landscape-level implementation strategies. Challenges like chemical contamination, limited access to certified organic inputs, and insufficient institutional and technical support leave many existing ORF initiatives vulnerable and constrain further expansion. To fully realise ORF’s resilience and sustainability potential, more targeted research and policy attention are needed. An integrated governance approach that considers both biophysical and socio-economic dimensions is essential to support a meaningful and scalable transition to organic rice farming in climate-sensitive regions like the MKD. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

This study aimed to assess the effect of organic amendment-based integrated nitrogen (N) application on major soil macronutrients, carbon (C) accumulation, crop productivity and N use efficiency (NUE) of different rice-based cropping patterns. This experiment was composed of various organic amendments ((i): control (no organic amendment, application of 100% N from urea); (ii): 25% N from compost + 75% N from urea; (iii): 25% N from cowdung + 75% N from urea; iv: 25% N from vermicompost + 75% N from urea) and rice-based cropping patterns ((i) rice–rice–rice, (ii) rice–fallow–rice–mustard, and (iii) rice–vegetables–rice). Organic amendments and soils (0–20 cm) were collected from farmers’ fields and were analyzed for major nutrients: N and organic C (OC), phosphorus (P), potassium (K) and sulphur (S). Soil OC accumulation potential, system productivity and partial factor productivity of N were also calculated. The results indicate that organic amendment application significantly enhanced soil OC (0.957–1.604%) compared to control (0.916–1.292%), with vermicompost attaining the highest OC content and OC accumulation potential (up to 24.15%), especially in the rice–vegetables–rice pattern. Vermicompost also predominantly increased N (22–62%) and S (51–78%) level in soils, while cowdung significantly amended P levels (155–178%) and contributed steadily to improved K levels in soil. Overall, nutrient improvements and soil fertility were highest under the rice–vegetables–rice system, followed by rice–fallow–mustard–rice and rice–rice–rice. System productivity was maximum in the rice–vegetables–rice pattern (up to 85.7 t ha⁻¹), with remarkable enhancements in NUE when organic amendments were applied. Cowdung and vermicompost both matched or exceeded the performance of chemical fertilizer in these cases. These results demonstrate the advantages of integrated N management and diversified cropping to improve nutrient cycling, soil health and sustainable productivity in rice-based agroecosystems. Full article

Achieving food security in Africa requires the sustainable intensification of cereal production, particularly for wheat, rice, and maize, which form the foundation of daily caloric intake in Africa. Smallholder farmers, who dominate cereal production in Africa, face challenges such as low productivity, limited resources, and varying climatic conditions. Remote sensing, specifically through Sentinel-2 satellite imagery, offers a cost-effective method to monitor and improve farming practices. This study evaluates the possibility of extracting spectral reflectance curves of cereal crops from Sentinel-2 imagery across 68 smallholder farms in Togo, Tunisia, and Tanzania from 2021 to 2023. The farms ranged in size from 1 to 2 ha. We also assessed the separability of reflectance values following improved management practices (IPs), which included optimized seeding, fertilization, and pest control, and traditional farmers’ practices (FPs), which are typically characterized by inconsistent plant spacing and sub-optimal fertilization and pest management. Additionally, we analyzed regional variability in reflectance values to understand how climatic and management differences affect crop performance. Results showed that Sentinel-2 successfully captured spectral reflectance curves in all the countries and delineated management practice differences in Togo and Tunisia. Full article

Pesticide usage in the integrated rice–crayfish system has aroused lots of attention all over the world. Especially in China, fish farmers often use copper sulfate and pendimethalin to remove moss from aquaculture water and glyphosate to remove weeds in and around crayfish–crab mixed culture ponds. To explore the stress response mechanism of CuSO₄, pendimethalin, and glyphosate to the hepatopancreas of Procambarus clarkii (Girard, 1852), seven treatment groups including control, CuSO₄ (1 and 2 mg·L⁻¹), pendimethalin (PND, 5 and 10 μg·L⁻¹), and glyphosate (5 and 10 μg·L⁻¹) experimental groups were set up; the transcriptome responses were detected at 4, 8, and 12 days, respectively. The irregular structure and vacuoles were shown in the hepatopancreas for 2 mg·L⁻¹ CuSO₄ and 10 μg·L⁻¹ glyphosate exposures at 12 d, while narrowed hepatic sinusoids were revealed after 10 μg·L⁻¹ pendimethalin exposure. The pathways of ribosome, lysosome, and peroxisome were significantly enriched for differential expression genes (DEGs); in addition, tyrosine metabolism, starch, and sucrose metabolism were enriched under the stress of the three inputs. Genes in related pathways such as glycerophospholipid metabolism, oxidative phosphorylation, and glycerolipid metabolism also changed, and the expression of genes associated with oxidative phosphorylation changed significantly under the stress of the three inputs. Oxidative stress, neurotoxicity, metabolism, and energy supply have been significantly affected by the above herbicide exposure. High concentrations and/or long-term duration exposure may result in metabolic disorders rather than eliminate toxicity through adaptability responses. Full article

The rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Crambidae), is one of the major pests in rice-growing areas. Its larvae feed on rice stems, causing symptoms of rice dead sheaths, dead hearts, and withered ears, resulting in heavy rice yield losses. Chemical insecticides remain the cornerstone of control strategies; however, the rapid development of resistance to multiple insecticide classes has emerged as a critical challenge to farmers and pest control specialists. Advanced methods utilizing molecular and gene sequence data from field-collected C. suppressalis populations, both resistant and susceptible, have provided a deeper understanding of the resistance mechanisms in this pest. Several components of Insecticide Resistance Management (IRM) programs serve as countermeasures to insecticide resistance in this pest. In the current review, we concentrate on insecticide resistance development, molecular mechanisms and resistance management of C. suppressalis. Full article

Real-time nitrogen (N) management based on the leaf color chart (LCC) is considered a potential alternative to traditional farmer practices. However, its physiological mechanisms for enhancing rice N utilization and its effects on paddy field N balance remain unclear. We aimed to elucidate the potential enzymatic mechanisms underlying LCC’s influence on rice N use and quantify the impact of LCC on paddy field N balance. In 2022 and 2023, a single-factor randomized block design experiment was conducted during the rice planting season. Four N treatments: no N (ONF), farmers’ conventional practices + urea [FNR] as the control, LCC + urea [SSNM1], LCC + controlled-release urea [SSNM2] were administered. Rice yield and N uptake were positive correlations with nitrate reductase (NR), glutamine synthetase (GS), glutamate-pyruvate transaminase (GPT), glutamate-oxaloacetate transaminase (GOT) and glutamate dehydrogenase (GDH) activities, which were higher under SSNM1 and SSNM2 compared with FNR, but were negative correlation with proteinase activity. Moreover, SSNM1 and SSNM2 increased rice yield by 9.2% and 9.4%, N uptake by 15.4% and 15.3%, and N use efficiency by 46.9% and 65.0%, and reduced reactive N losses by 46.2% and 66.7%, respectively. The annual net soil N inputs under FNR, SSNM1, and SSNM2 were 12.6, 8.9, and 4.2 kg N ha⁻¹, respectively. LCC-based N management increased N uptake and rice yield by enhancing the activities of NR, GS, GPT, GOT, and GDH while reducing protease activity. Moreover, LCC maintained soil N supply capacity even with reduced nitrogen fertilizer application. Full article

China aims to reduce carbon emissions but faces challenges from small-scale farmer operations. Previous studies have predominantly examined carbon density using macro-level data. This study employs a primary field survey involving 916 rice farmers, along with input–output data from their typical paddy plots, to calculate micro-level carbon emissions and assess the impact of land operation scale. The results indicate that operational scale enhances carbon emission productivity and has a nonlinear relationship with carbon emission intensity. From survey data, the carbon emission intensity of late rice is 4648.77 kg CO_2eq·ha⁻¹ in Guangdong province China, which differs by a mere 1.14% from the figure derived from yearbook macro data. The yield carbon emission productivity and yield value carbon emission productivity of rice production are 1.347 kg·kg CO_2eq⁻¹ and 2.166 CNY·kg CO_2eq⁻¹, respectively. The operational scale significantly positively enhances indirect carbon emission productivity, a key indicator of economic growth and environmental sustainability. However, it exhibits a U-shaped effect on carbon emission intensity. Our results underscore the critical role of expanding the operational scale among individual farmers to boost carbon emission productivity, facilitating the simultaneous development of grain crops and a reduction in carbon emissions. Full article