● Extreme precipitation has a greater impact on the agricultural water scarcity index (AWSI) than extreme temperature. ● AWSI assessment at a tertiary basin level better captured the influence of climate extremes than at a secondary basin level. ● AWSI was about 28% higher than the long-term average during dry years and 55% higher under exceptionally dry conditions at a tertiary basin level. ● Compound dry and hot conditions increased AWSI by more than 60% in some regions.
Amid the escalating frequency of climate extremes, it is crucial to determine their impact on agricultural water scarcity to preserve agricultural development. Current research does not often examine how different spatial scales and compound climate extremes influence agricultural water scarcity. Using an agricultural water scarcity index (AWSI), this study examined the effects of precipitation and temperature extremes on AWSI across secondary and tertiary river basins in China from 1971 to 2010. The results indicated a marked increase in AWSI during dry years and elevated temperatures. The analysis underscores that precipitation had a greater impact on AWSI than temperature variation. In secondary basins, AWSI was about 26% higher than the long-term average during dry years, increasing to nearly 49% in exceptionally dry conditions. By comparison, in tertiary basins, the increases were 28% and 55%, respectively. In hot years, AWSI rose by about 6.8% (7.3% for tertiary basins) above the average, surging to about 19.1% (15.5% for tertiary basins) during extremely hot periods. These results show that AWSI assessment at the tertiary basin level better captured the influence of climate extremes on AWSI than assessments at the secondary basin level, which highlights the critical importance of a finer spatial scale for a more precise assessment and forecast of water scarcity within basin scales. Also, this study has highlighted the paramount urgency of implementing strategies to tackle water scarcity issues under compound extreme dry and hot conditions. Overall, this study offers an in-depth evaluation of the influence of both precipitation and temperature variation, and research scale on water scarcity, which will help formulate better water resource management strategies.
● Evidence for interplant communication via common mycorrhizal networks is reviewed. ● Potential transport routes for semiochemicals via fungal hyphae are identified. ● Drivers of signal exchange via CMNs are discussed.
Interplant communication is of vital importance for plant performance in natural environments. Mycorrhizal fungi have emerged as key contributors to the below ground communication between plants. These mutualistic fungi form connections between the roots of plants via their hyphae, known as common mycorrhizal networks (CMNs). These hyphal networks are thought to be important ways for the exchange of signals between plants. This paper reviews the evidence for CMN-based transfer of semiochemicals between plants upon exposure to pathogen infection, herbivory or mechanical damage. Potential transport routes are explored, asking whether the fungi can actively contribute to the distribution of such signals within the network and discussing potential drivers for signal exchange. It is concluded that identification of the signals that are exchanged remains an important challenge for the future.
● Drying is a crucial postharvest process for paddy grain and impacts the quality of both paddy and rice. ● A deep bed dryer is a convective dryer that relies on airflow, temperature and relative humidity as the primary drying parameters. ● An aeration system is necessary to distribute the drying air evenly throughout the drying chamber. ● The optimal aeration system was determined using computational fluid dynamics and the AHP-TOPSIS method. ● The most optimal aeration system is a model deep bed dryer with a sloping floor and circular pipe formation.
In the context of food security, drying is a crucial postharvest process for paddy grain because it significantly impacts the quality of both paddy and rice. To conserve energy during the drying process, deep bed dryers are used as convective dryers that use a combination of ambient airflow and heating, thus relying on airflow, temperature, and relative humidity (RH) as the primary drying parameters. Consequently, an aeration system is necessary so that the drying air can penetrate the thick pile of paddy grain and distribute evenly throughout the drying chamber. This analysis aimed to determine the most optimal aeration system by using computational fluid dynamics (CFD) and the AHP-TOPSIS method. The quantitative and visual analysis of the airflow velocity, pressure, temperature, and RH was conducted using CFD on four different dryer aeration systems models, which were then ranked by preference value using the AHP-TOPSIS method. Model 4, with a sloping floor and circular pipe formation, was found to have the most optimal aeration system (preference value of 0.788) for a paddy grain deep bed dryer prototype.
● A new vegetation index, rice blast index (RBI), was constructed to detect rice leaf blast. ● The disease detection performance of RBI, TVI, DDI and MTVI1 vegetation indices were compared. ● The level of leaf blast disease in the field was evaluated using the new RBI.
Leaf blast is a significant global problem, severely affecting rice quality and yield, making swift, non-invasive detection crucial for effective field management. This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops. ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index, the rice blast index (RBI). This RBI was compared with 30 established vegetation indexes, using correlation analysis and visual comparison to further shortlist six superior indexes, including RBI. These were evaluated using the K-nearest neighbor (KNN) and random forests (RF) classification models. RBI demonstrated superior detection accuracy for leaf blast in both the KNN model (95.0% overall accuracy and 93.8% kappa coefficient) and the RF model (95.1% overall accuracy and 92.5% kappa coefficient). This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection, offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation.
● Plants can respond to heterogeneously distributed nutrient resources by enhancing root foraging capacity. ● Incremental amplification of root foraging for nutrients induced by localized fertilization was proposed. ● Incremental effects from the roots/rhizosphere to the plant-soil system conserve resources and reduce the environmental footprint of agricultural production.
Localized fertilization strategies (banding fertilizers) developed to minimize nutrient fixation by soil are used widely in intensive agricultural production. Localized fertilization encourages root foraging for heterogeneously distributed soil nutrients. This review focuses on the advances in root growth and nutrient acquisition of heterogeneously distributed soil resources. It is proposed that the incremental amplification of root foraging for nutrients induced by localized fertilization: (1) increased absorption area due to altered root morphology, (2) enhanced mobilization capacity underpinned by enhanced root physiological processes, and (3) intensified belowground interactions due to selective stimulation of soil microorganisms. The increase in root proliferation and the nutrient mobilization capacity as well as microbiome changes caused by localized fertilization can be amplified stepwise to synergistically enhance root foraging capacity, nutrient use efficiency and improve crop productivity. Engineering the roots/rhizosphere through localized, tailored nutrient application to stimulate nature-based root foraging for heterogeneously distributed soil nutrients, and scaling up of the root foraging capacity and nutrient acquisition efficiency from the rhizosphere to the field offers a potential pathway for green and sustainable intensification of agriculture.
● 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.
● 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.
● 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.
● 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.
