The manuscript revealed the ameliorative effects of exogenous melatonin in two distinct reproductive stages, i.e., developing grains (20 days after pollination) and matured grains (40 days after pollination) in two contrasting indica rice genotypes, viz., Khitish (arsenic-susceptible) and Muktashri (arsenic-tolerant), irrigated with arsenic-contaminated water throughout their life-cycle. Melatonin administration improved yield-related parameters like rachis length, primary and secondary branch length, number of grains per panicle, number of filled and empty grains per panicle, grain length and breadth and 1000-grain per weight. Expression of GW2, which negatively regulates grain development, was suppressed, along with concomitant induction of positive regulators like GIF1, DEP1 and SPL14 in both Khitish and Muktashri. Melatonin lowered arsenic bioaccumulation in grains and tissue biomass, more effectively in Khitish. Unregulated production of reactive oxygen species, leading to cellular necrosis caused by arsenic, was reversed in presence of melatonin. Endogenous melatonin level was stimulated due to up-regulation of the key biosynthetic genes, SNAT and ASMT. Melatonin enhanced the production of diverse antioxidants like anthocyanins, flavonoids, total phenolics and ascorbic acid and also heightened the production of thiol-metabolites (cysteine, reduced glutathione, non-protein thiols and phytochelatin), ensuring effective chelation and arsenic detoxification. Altogether, our observation, supported by principal component analysis, proved that melatonin re-programs the antioxidative metabolome to enhance plant resilience against arsenic stress to mitigate oxidative damages and reduce arsenic translocation from the soil to tissue biomass and edible grains.

UNASSIGNED: With increasing labor shortage and production costs, water scarcity and climate change, there is increased interest in ratooning as a green, resource-efficient technology to boost sustainable rice production, especially in China. Since the performance of ratoon rice (regenerating a second crop from the stubble left in the fields after the main harvest) and the impact of agronomic practices on its yield have shown mixed results across the world, a better understanding is needed to determine under which conditions ratoon rice performs well.
UNASSIGNED: The objectives are (i) to quantify variation in rice yield of main and ratoon crops, (ii) to assess genetic variation in and impact of agronomic practices on rice yield, focusing on the yield of ratoon crop and total yield (main and ratoon crops), and (iii) review of economic and environmental benefits of ratoon rice in comparison with single and double rice cropping.
UNASSIGNED: In researching ratoon rice, we compiled a database from 68 studies published from 2000 to 2023. Descriptive data analysis was performed.
UNASSIGNED: Studies from non-tropical regions account for about 70%. Large variation exists in the yield of ratoon crop across the studies, with lower yield from the tropics than non-tropics. The ratio of yield of ratoon crop to that of main crop also varied widely from 0.13 to 0.67 with 0.36 and 0.5 in tropics and non-tropics, respectively. The yield of ratoon crop was positively related to the yield of main crop, crop duration and nitrogen fertilizer application rate, which were generally higher in non-tropics. Hybrid varieties out-yielded inbred varieties in both main and ratoon crops in non-tropical regions. Direct seeding and AWD had a positive impact on the yield of ratoon crop. The impact of stubble cutting height was mixed. While agronomic nitrogen use efficiency (AEN) during entire ratoon rice cropping was similar to that reported for single rice cropping in previous studies, AEN for ratoon crop in tropical regions tended to be lower than those from previous studies on single rice cropping. Ratoon rice cropping reduced labor input and production cost and increased net economic return compared with double rice cropping.
UNASSIGNED: We propose a research agenda, with the focus on improvement of genetic and agronomic practices to explore the potential of ratoon rice cropping, especially in the tropics.
UNASSIGNED: This study provides insight into the progress in ratoon rice research over the past two decades globally, and specifically in the tropics.

Crop phenomics enables the collection of diverse plant traits for a large number of samples along different time scales, representing a greater data collection throughput compared with traditional measurements. Most modern crop phenomics use different sensors to collect reflective, emitted, and fluorescence signals, etc., from plant organs at different spatial and temporal resolutions. Such multi-modal, high-dimensional data not only accelerates basic research on crop physiology, genetics, and whole plant systems modeling, but also supports the optimization of field agronomic practices, internal environments of plant factories, and ultimately crop breeding. Major challenges and opportunities facing the current crop phenomics research community include developing community consensus or standards for data collection, management, sharing, and processing, developing capabilities to measure physiological parameters, and enabling farmers and breeders to effectively use phenomics in the field to directly support agricultural production.

Plant pathogens can decimate crops and render the local cultivation of a species unprofitable. In extreme cases this has caused famine and economic collapse. Timing is vital in treating crop diseases, and the use of computer vision for precise disease detection and timing of pesticide application is gaining popularity. Computer vision can reduce labour costs, prevent misdiagnosis of disease, and prevent misapplication of pesticides. Pesticide misapplication is both financially costly and can exacerbate pesticide resistance and pollution. Here, we review the application and development of computer vision and machine learning methods for the detection of plant disease. This review goes beyond the scope of previous works to discuss important technical concepts and considerations when applying computer vision to plant pathology. We present new case studies on adapting standard computer vision methods and review techniques for acquiring training data, the use of diagnostic tools from biology, and the inspection of informative features. In addition to an in-depth discussion of convolutional neural networks (CNNs) and transformers, we also highlight the strengths of methods such as support vector machines and evolved neural networks. We discuss the benefits of carefully curating training data and consider situations where less computationally expensive techniques are advantageous. This includes a comparison of popular model architectures and a guide to their implementation.

Each new generation of grassland managers could benefit from an improved understanding of how modification of nitrogen application and harvest dates in response to different weather and soil conditions will affect grass yields and quality. The purpose of this study was to develop a freely available grass yield simulation model, validated for England and Wales, and to examine its strengths and weaknesses as a teaching tool for improving grass management. The model, called LINGRA-N-Plus, was implemented in a Microsoft Excel spreadsheet and iteratively evaluated by students and practitioners (farmers, consultants, and researchers) in a series of workshops across the UK over 2 years. The iterative feedback led to the addition of new algorithms, an improved user interface, and the development of a teaching guide. The students and practitioners identified the ease of use and the capacity to understand, visualize and evaluate how decisions, such as variation of cutting intervals, affect grass yields as strengths of the model. We propose that an effective teaching tool must achieve an appropriate balance between being sufficiently detailed to demonstrate the major relationships (e.g., the effect of nitrogen on grass yields) whilst not becoming so complex that the relationships become incomprehensible. We observed that improving the user-interface allowed us to extend the scope of the model without reducing the level of comprehension. The students appeared to be interested in the explanatory nature of the model whilst the practitioners were more interested in the application of a validated model to enhance their decision making.

Groundwater discharge exceeding recharge threatens sustainable aquifer water use internationally. Interest remains high in discovering more hydrologically sustainable and economically affordable measures to protect these aquifers. Previous research has conducted various aquifer assessments. Some work has investigated costs and benefits of various plans that would limit aquifer pumping. Despite notable advances in this kind of analysis, little published work to date has unified these elements into a science-based integrated framework to inform more sustainable aquifer policy design. This work\'s novel contribution is to integrate analysis of hydrology, economics, institutions, and policy into a unified scientific framework to inform choices on more sustainable pumping strategies while protecting economic activity for agricultural and urban water-using sectors. It does so by conceptualizing, formulating, designing, and applying a mathematical programming framework to replicate historically observed pumping patterns in parts of the Southern and Central High Plains Ogallala Aquifer region in New Mexico, USA. We first calibrated the optimization framework to replicate the historically observed data. We then go on to identify least cost pumping caps that would have partly restored the aquifer to its 2014 level by 2020, while comparing the performance of four other partial aquifer protection policy measures. Findings indicate a surprisingly low cost that could have been incurred to partially protect the aquifer over that period. However, these low costs are complicated by (1) decreasing water quality outside of the irrigated regions and (2) focusing of lateral inputs to a narrower zone of depression around the irrigated regions. These findings carry important implications for identifying more sustainable aquifer management plans internationally. The work\'s importance comes from its capacity to inform policy debates over a range of water shortage sharing plans, while respecting institutional constraints governing equitable burden sharing.

Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world\'s population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L\'Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.

Long-term application of organic and mineral fertilizers can lead to changes in the elemental composition of agroecosystem components. Both the levels of nutrients and potentially toxic elements can change, as can the potential for these elements to be available to plants through changes in soil properties. Soil and plant samples of two species (pea Pisum sativum L. and oat Avena sativa L.) were collected from plots of a long-term field experiment on the application of mineral and organic fertilizers and their combinations to gray forest soils in the Vladimir region, Russia. Soil samples from the 0-20 and 20-40 cm layers were subjected to acid digestion to determine total element content. Mobile forms of elements were extracted from topsoil samples using acetate-ammonium buffer (pH 4.8). Sample preparation of pea and oat plant organs (stems, leaves, pods/ears) included sample digestion in a microwave sample digestion system ETHOS EASY (Milestone, Italy). The elemental composition of the samples was determined by inductively coupled plasma optical emission spectrometry (ICP-OES) using Agilent 5800 ICP-OES (Agilent Technologies, USA). The dataset includes concentration data for 34 elements, including rare earth elements, in these samples collected in 2021. The dataset also contains general agrochemical characteristics of soils of the experimental groups: pH of water and salt suspension, organic carbon content, mobile forms of phosphorus. The data can be valuable to researchers developing fertilizer application systems and modeling changes in the elemental composition of agroecosystems.

The depletion of water resources has gained global attention, particularly in arid climates, where there is growing interest in reusing treated wastewater for irrigation. This study focuses on the impact of irrigating treated wastewater using a hybrid multi-soil-layering (MSL) technology on soil physicochemical properties and the agro-physiological characteristics of maize (Zea mays) cultivated in Morocco, a region characterized by arid conditions. To achieve this research goals, three plots were cultivated with Zea mays and subjected to irrigation with water of varying qualities: raw wastewater (RWW), treated wastewater (TWW), and well water (WW). This experiment ran for five months, covering one crop season. The physicochemical and microbiological parameters in the soil and water were investigated, and the agro-physiological characteristics of the maize crops were assessed. The findings revealed significant differences in physicochemical and microbial parameters within both water and soil, as well as in the physiological responses of the maize crop, among the three water treatments. TWW\'s quality met the permissible limits for direct wastewater discharge, as prescribed by Moroccan norms, making it suitable for potential irrigation reuse. Moreover, the higher content of key elements (Na, K, Ca, and Mg) in WW indicated that TWW was more suitable for irrigation. Zea mays irrigated with RWW and TWW exhibited a higher accumulation of protein and sugar content compared to WW irrigation. Furthermore, the biomass parameters, including root, aerial, and grain dry weight, showed a positive effect on Zea mays irrigated with RWW and TWW compared to WW. Total chlorophyll content, on the other hand, was highest in plants irrigated with WW, followed by TWW. Plants irrigated with RWW produced the highest amounts of nitrogen, phosphorus, and potassium. Conversely, plants irrigated with WW had a higher content of Ca, Na, and Mg. TWW yielded medium concentrations of N, P, K, Ca, Mg, and Na compared to RWW and WW, attributed to the nutrients provided by irrigation with TWW using the hybrid MSL technology. In conclusion, aside from their use as irrigation water, treated wastewater emerges as a valuable source of plant nutrients and soil fertilizers. They offer significant nutritive value, enhancing plant growth, reducing the need for additional fertilizer application, lowering mineral fertilization costs, and increasing the productivity of infertile soils. This highlights the potential of treated wastewater to improve agricultural sustainability in arid regions like Morocco.

<b>Background and Objective:</b> This plant is known in many parts of the world and is used as medicine in some countries. In Vietnam, people know how to use <i>Passiflora foetida </i>L.,<i> </i>as an herb. There are many research works in the world, most of them focus on medicinal properties. The objective of this study was to determine the genetic diversity of samples of <i>Passiflora foetida </i>L., yellow fruit based on agronomic traits and the gene region of \"<i>rbcL</i>\". <b>Materials and Methods:</b> Seed samples were arranged in a completely randomized design with three replications. The trial was a sample of <i>Passiflora foetida </i>L., species. The distance between sample plant is 3×2 m, so the total number of trees is 1,330 trees ha¹. The genetic relationship between them was determined through the construction of a phylogenetic tree in the \"<i>rbcL</i>\" gene region sequence. Data analysis and processing methods done by using X software. <b>Results:</b> The PCR results that amplify the \"<i>rbcL</i>\" gene region around 670 bp, the PCR products were then sequenced. The sequence results were compared with the sequences on the NCBI gene bank, showing that the sequences of the nine varieties/species all coincided with the <i>Passiflora foetida </i>Sims species sequences with a high similarity coefficient from 98.77 to 99.9%. Genetically shows that all nine species samples belong to <i>Passiflora foetida</i> which can be classified into three genetic samples of An Giang (LA1), Ca Mau (LA2) and Can Tho (LA3) which are genetically close to each other, have the same index. <b>Conclusion:</b> The heritability in the broad sense is high in the two traits of stem height and leaf length, so it can be considered that these two traits are controlled by genes. The cultivation of algae seeds to improve the productivity of these medicinal plant varieties is important in providing raw materials for the oriental medicine industry.