● Two bean cultivars responded strongly to Rhizobium inoculation in both 2012 and 2013, relative to uninoculated control. ● Inoculating bean with strain HB-429 increased shoot biomass, nodule number, and nodule dry matter per plant. ● Rhizobial inoculation of bean increased pod number per plant, seed number per pod, and grain yield. ● Applying P to bean increased shoot biomass, nodule number, and nodule dry matter per plant. ● The combined use of Rhizobium inoculation and low P application is recommended for bean production in Ethiopia.
Bean (Phaseolus vulgaris) yields in Africa can be increased through the application of phosphorus and nitrogen fertilizers, as both nutrients are low in African soils. However, using greener technologies is preferred to mineral fertilizers for maintaining soil health. In this study, Rhizobium inoculation and moderate P supply (0, 10, 20, and 30 kg·ha−1) to two bean cultivars were evaluated in consecutive years at Hawassa for their effects on plant growth, nodulation, and grain yield. The results showed that, relative to the uninoculated control, the two bean cultivars responded strongly to Rhizobium inoculation, with strain HB-429 outperforming strain GT-9 in both 2012 and 2013. Shoot biomass, nodule number and nodule dry matter per plant were increased by 9%, 40%, and 54%, respectively, in 2012, and by 20%, 39%, and 13% in 2013 with strain HB-429 inoculation. This resulted in increased pod number per plant, seed number per pod and grain yield by 56%, 51%, and 49% in 2012, and by 38%, 25%, and 69% in 2013, respectively, with strain HB-429 inoculation. Bean inoculation with GT-9 also increased grain yield by 35% and 68% in 2012 and 2013, respectively. Applying 10–30 kg·ha−1 P to bean cultivars increased shoot biomass, nodule number, and nodule dry matter per plant by 7% to 39%, 23% to 59%, and 59% to 144% in 2012, respectively, and by 10% to 40%, 21% to 43%, and 12% to 35% in 2013, respectively. Relative to the zero-P control, adding only 10 kg·ha−1 P increased pod number per plant, seed number per pod, and grain yield by 10%, 30%, and 61% in 2012, and by 11%, 11%, and 38% in 2013, respectively. The combined use of Rhizobium inoculation with low P application (20 kg·ha−1) was found to increase bean production in Ethiopia and is thus recommended to resource-poor farmers.
● Developed a novel solar-powered corona dielectric barrier discharge (cDBD) microreactor for sustainable agriculture. ● cDBD microreactor lowers pH and elevates oxidation-reduction potential, nitrite, and nitrate concentrations in plasma-activated water (PAW). ● PAW treatment doubled spinach seedling growth and increased germination rates by up to 135%. ● PAW modulates germination-related hormones to enhance aged-seed rejuvenation and growth.
Seed aging adversely affects agricultural productivity by reducing germination rates and seedling vigor, leading to significant costs for seed banks and companies due to the need for frequent seed renewals. This study demonstrated the use of plasma-activated water (PAW), generated by a solar-powered corona dielectric barrier discharger, to enhance germination rates of spinach seeds that had been stored at 4 °C for 23 years. Treating seeds with PAW at 17 kV for 15 min improved germination (by 135%) and seedling growth compared to untreated seeds. Through detailed analysis, beneficial PAW properties for seed development were identified, and a molecular mechanism for this rejuvenation is proposed. The solar-powered microreactor used in this study is considered to represent a significant advancement in seed treatment technology, offering a sustainable solution to meet growing food demands while addressing environmental and resource sustainability challenges.
● Biochar-compost-based controlled-release urea fertilizer (BCRUF) pellets with an active microbial community were successfully synthesized. ● The releasing time of 80% N in BCRUF was 4–6 h in the water and 192 h (8 days) in soil. ● Processing parameters of BCRUF fabrication was influencing the microbe populations in the pellets. ● The BCRUF showed very promising characteristics to improve NUE and sustainability in agricultural production.
Nitrogen (N) fertilizers in agriculture suffer losses by volatilization of N to the air, surface runoff and leaching into the soil, resulting in low N use efficiency (NUE) (
● Sustainable approach to minimize pesticide usage and enhance crop productivity was developed. ● Disease management in cauliflower achieved by integrating spectral sensor, machine learning, and targeted spraying. ● Support vector machine outperformed the decision trees model in black rot detection in cauliflower. ● Targeted spraying cut chemical use by 72.5% and saved 21.0% time in black rot-infested crops.
This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine (SVM) algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer (50‒150 L·ha‒1), equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input application. Additionally, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management.
● Global black soil distribution map developed by using country-driven approach. ● Black soils are key to global food security and climate change mitigation and adaptation. ● Black soils form under various pedoenvironments at global level. ● Black soils predominantly occur in Eastern Europe, Central and Eastern Asia, and the northern and southern extremities of the Americas. ● Black soils hold a substantial global soil organic carbon stock, amounting to about 56 Gt.
Black soils, characterized by their thick, dark horizons enriched with organic matter, epitomize highly fertile soils. However, their fertility precipitates intense land use, engendering challenges such as soil erosion, nutrient depletion, pollution, compaction, salinization, and acidification. Notably, these soils are significant contributors to global greenhouse gas emissions, primarily due to substantial losses in soil organic carbon. Despite these challenges, black soils are pivotal for global food production. This paper delineates the implementation of digital soil mapping for the global cartography of black soils and human interference on these soils. Predominantly distributed in Eastern Europe, Central and Eastern Asia, and North and South America, black soils cover an approximate area of 725 Mha, with the Russian Federation, Kazakhstan, and China collectively have over half of this area. Agriculturally, these soils underpin significant proportions of global crop yields, producing 66% of sunflower, 30% of wheat, and 26% of potato outputs. The organic carbon content in the upper 30 cm of these soils is estimated at 56 Gt. Sustainable management of black soils is imperative for ensuring food security and addressing climate change on a global scale.
● A specific examination of the livelihood transition within a lakeside community in China was undertaken. ● The analysis centered on examining the process of livelihood changes from both institutional and action perspectives. ● Improving locas’ physical assets in tourism development can boost their property-based income and facilitate sustainable transformation. ● The analysis highlighted the crucial role of human capital in the livelihood transition process, emphasizing the significance of talent and innovative models in ensuring its sustainability.
Fisheries in coastal and lakeside regions are increasingly facing sustainability challenges. This predicament has compelled these regions to shift toward economic diversification, with tourism emerging as a feasible alternative economic activity. This study focuses on a rural community adjacent to Erhai Lake in Dali City, Yunnan Province, China, examining its shift from a fishing-based economy to tourism over several decades. Employing an adaptive sustainable livelihood framework, this study assessed the livelihood transformation across various stages over an extended period, from both institutional and action-oriented perspectives, analyzing factors influencing sustainable livelihood transformation in lakeside communities and their subsequent effects. This research revealed several key insights. Firstly, tourism, as an alternative industry to fishing, not only faces increasingly stringent environmental protection policies but also confronts multiple challenges from the community level. Secondly, the improvement of the physical assets of locals within the tourism development, which can increase property-based income, has the potential to facilitate a sustainable transformation of their livelihoods. Thirdly, analysis identifies the pivotal role of human capital in the current transition process, with the influence of talent and innovative livelihood industry management models gaining prominence to ensure sustainability of this transformation.
● Soil compaction due to intensive agriculture threatens soil quality, crop growth, and food security. ● Study explores the factors contributing to compaction, aiming to develop effective mitigation methods. ● The goal is to reduce soil compaction, improve soil quality, boost crop yield and enhance agricultural sustainability. ● Innovations needed to address soil compaction in modern agriculture.
With the development of agricultural technology to meet the growing demands of a rapidly increasing population and economic development, intensive agriculture practices have been widely adopted globally. However, this intensification has resulted in adverse consequences for soil structure due to intensified farming activities and increased usage of heavy farm machinery. Of particular concern is soil compaction, which leads to the degradation of physical, chemical and biological properties of the soil. Soil compaction negatively impacts crop growth, reduces yields and poses a significant threat to food security and the overall sustainability of agricultural systems. Recognizing these challenges, this review aims to deepen understanding of the factors contributing to soil compaction and to develop effective mitigation strategies. By doing so, it is intended to attenuate the adverse impacts of soil compaction, improve soil structure, increase crop yield and ultimately enhance the sustainability of agricultural practices.
● 13C isotope analysis was used to estimate the contribution of new and old carbon to SOC. ● The maize plot with high N rate improved SOC fixation than the maize plot with low N rate. ● The maize plot with high N rate transferred organic matter to a deeper soil layer. ● There are remarkable differences in turnover time of SOC under different N rates.
Empirical research indicates that heightened soil nitrogen availability can potentially diminish microbial decomposition of soil organic carbon (SOC). Nevertheless, the relationship between SOC turnover response to N addition and soil depth remains unclear. In this study, soils under varying N fertilizer application rates were sampled up to 100 cm deep to examine the contribution of both new and old carbon to SOC across different soil depths, using a coupled carbon and nitrogen isotopic approach. The SOC turnover time for the plot receiving low N addition (250 kg·ha−1·yr−1 N) was about 20−40 years. Conversely, the plot receiving high N (450 kg·ha−1·yr−1 N) had a longer SOC turnover time than the low N plot, reaching about 100 years in the upper 10−20 cm layer. The rise in SOC over the entire profile with low N addition primarily resulted from an increase in the upper soil (0−40 cm) whereas with high N addition, the increase was mainly from greater SOC in the deeper soil (40−100 cm). Throughout the entire soil layer, the proportion of new organic carbon derived from maize C4 plant sources was higher in plots treated with a low N rate than those treated with a high N rate. This implies that, in contrast to low N addition agricultural practices, high N addition predominantly enhances the soil potential for fixing SOC by transporting organic matter from surface soils to deeper layers characterized by more stable properties. This research offers a unique insight into the dynamics of deep carbon under increased N deposition, thereby aiding in the formulation of policies for soil carbon management.
● Machine learning offers innovative and sustainable weed management approaches. ● Herbicide use and environmental impact can be reduced through machine learning. ● Machine learning models can classify weed species and optimize herbicide usage. ● Real-time monitoring of invasive species is possible with machine learning.
Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and profitability. Long-established methods of weed control, such as manual labor and synthetic herbicides, have been widely used but come with their own set of challenges. These methods are often time-consuming, labor-intensive, and pose environmental risks. Herbicides have been the primary method of weed control due to their efficiency and cost-effectiveness. However, over-reliance on herbicides has led to environmental contamination, weed resistance, and potential health hazards. To address these issues, researchers and industry experts are now exploring the integration of machine learning into chemical weed management strategies. As technology advances, there is a growing interest in exploring innovative and sustainable weed management approaches. This review examines the potential of machine learning in chemical weed management. Machine learning offers innovative and sustainable approaches by analyzing large data sets, recognizing patterns, and making accurate predictions. Machine learning models can classify weed species and optimize herbicide usage. Real-time monitoring enables timely intervention, preventing invasive species spread. Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices, reducing herbicide usage and minimizing environmental impact. Validation and refinement of these algorithms are needed for practical application.
● Effects of the combination of drip irrigation and mulching practices on SE characteristics in a young orchard were investigated. ● Mulching treatments significantly affected daily SE and SWCs dynamics of the young orchard. ● Daily SE under FM and SM treatments was more susceptible to be affected by meteorological factors. ● SM is considered to be a more effective mulching practice for reducing unproductive SE and improving SWC status in young orchard with DI.
Soil evaporation (SE) is a key component of regional hydrological balance, especially in arid areas. China has the largest area of apple orchards in the world, but the effects of mulching practices on SE dynamics and their controlling factors remain poorly understood in orchards using drip irrigation (DI). This study was conducted to address these issues by measuring SE, meteorological factors, soil temperature (ST), and soil water content (SWC) in young apple orchard under two mulching treatments during the growing season. Field experiments, which included three treatments—film mulching (FM) and maize straw mulching (SM), and clean tillage (TL) as a comparator—were conducted in 3-year-old apple orchard with DI in arid northwestern China. The results revealed that mulching significantly affected the daily SE dynamics of the young orchard (p < 0.05), and the daily mean SE under FM, SM, and TL treatments was about 1.3 ± 0.5, 1.3 ± 0.4, and 1.7 ± 0.4 mm·d−1, respectively. No significant differences were detected in the daily SE between FM and SM treatments (p > 0.05), whereas the daily SWC in the four soil layers to 120 cm were consistently greater under SM treatment than under FM and TL treatments (p < 0.05). Compared to the TL treatment, the daily SE under FM and SM treatments was more susceptible to meteorological factors. Stepwise regression analysis showed that the daily SE of the young orchard was mainly controlled by the vapor pressure deficit, reference evapotranspiration and solar radiation, regardless of the treatment. However, there was no significant relationship between the daily SE and wind speed under TL treatment (p > 0.05). This study highlighted the significant differences in SE losses and SWC dynamics of the mulching treatments. Overall, SM is considered to be a more effective mulching practice for reducing unproductive SE and improving SWC status in young apple orchards with DI in arid and similar climatic regions.
