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This study evaluated seven vegetation indices for the monitoring of a cherry tomato crop using an unmanned aerial vehicle with a multispectral camera that measures in the green, red, and near-infrared spectral bands. A photogrammetric flight plan was designed to capture the spectral images every 2 weeks in two agricultural parcels identified as Treatment 1 (T1{T}_{1}) and Treatment 2 (T2{T}_{2}). The corresponding orthophotographs were obtained using digital photogrammetry techniques. Subsequently, vegetation indices were calculated for these orthophotographs. The mean and standard deviation of these indices were extracted, and a statistical analysis was performed to compare the vegetation indices and to analyze their behavior over time. Analysis of variance showed that the ratio vegetation index (RVI), green vegetation index (GVI), normalized difference vegetation index (NDVI), infrared percentage vegetation index (IPVI), green normalized difference vegetation index (GNDVI), and optimized soil-adjusted vegetation index (OSAVI) indices showed significant variation (P-value

Microplastics (MPs) have emerged as one of the global principal marine environmental problems in many countries, including Indonesia. However, fewer studies have been conducted on the MP impact on aquatic organisms. Thus, the present case study aimed to investigate the presence of MPs in mangrove sediments and coral reef fish in Biawak Island, West Java, Indonesia.

Tiger puffer is a commercially important fish cultured in high-density environments, and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding. However, the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments. The farmed tiger puffer detection model, called knowledge aggregation YOLO (KAYOLO), fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem. To alleviate feature loss caused by target blurring, we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer's features and improve detection precision. To address missed detection caused by mutual occlusion in high-density farming environments, a prediction box aggregation method, aggregating prediction boxes of the same object, was proposed to reduce the influence among different objects to improve detection recall. To validate the effectiveness of the proposed methods, ablation experiments, model performance experiments, and model robustness experiments were designed. The experimental results showed that KAYOLO's detection precision and recall results reached 94.92% and 92.21%, respectively. The two indices were improved by 1.29% and 1.35%, respectively, compared to those of YOLOv5. Compared with the recent state-of-the-art underwater object detection models, such as SWIPENet, RoIMix, FERNet, and SK-YOLOv5, KAYOLO achieved 2.09%, 1.63%, 1.13% and 0.85% higher precision and 1.2%, 0.18%, 1.74% and 0.39% higher recall, respectively. Experiments were conducted on different datasets to verify the model's robustness, and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5. The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects. Additionally, the model had a strong generalization ability on different datasets, indicating that the model can be adapted to different environments, and it has strong robustness.

The purpose of the current study is to investigate the qualitative characterization of nine different pure vegetable oil samples using dielectric spectroscopy which is a vastly resourceful and reasoned technique in the temperature range 0 ℃ to 25 ℃. Time-domain reflectometry technique is applied up to the microwave frequencies of 50 GHz for the first time for qualitative characterization of the selected vegetable oil samples with a special focus on the variances of dielectric properties like dielectric permittivity (ε′), dielectric loss (ε″), relaxation time concerning temperature and other physiochemical properties of the vegetable oil specimens.The experimental methodology involves the use of time-domain reflectometry (TDR) measurements up to the scale of 50 GHz done to analyse the aspects like lower and higher scales of values towards the static dielectric permittivity (εs) and relaxation time (τ) (ps) to further meaningfully compare and correlate this values with the fatty acid profiles of each of the nine vegetable oil samples to reason and draw comparative inferences about the quality aspects of vegetable oils. Microwave TDR studies provide an effective, alternate, simple, rapid, and viable way to exercise quality control and actuate data regarding the quality status of vegetable oils. Variances of dielectric permittivity (ε′) concerning dielectric loss (ε″) are graphically interpreted using the Cole Davidson model. The static dielectric permittivity (εs) was further recertified and measured accurately by using a precision LCR meter. Thermodynamic properties of all the nine vegetable oil samples like enthalpy (ΔH) (kJ/mol) and entropy of activation (ΔS) (J/mol ∙ K) are also calculated to further insight the dependence of dielectric properties of these oil samples concerning temperature.This dielectric spectroscopic study affirms the association of the quality aspects of these nine vegetable oil samples with their dielectric properties by providing meaningful correlations, comparatives and concurrencies of dielectric properties concerning the physiochemical properties which are a part of fatty acid profiles of these samples, which is a novel aspect of this study. The Cole-Cole plot underlines the tendency of realignment of dipoles as per the applied field. The complex permittivity spectra indicate the dwindling nature of molecular alignment including a slow decline to average coinciding values depending on the molecular bonding pattern of vegetable oil samples. The activation energy (ΔH) in (kJ/mol) is calculated for all the samples which are indicative of endothermic nature which experimentally proves that high energy is required for rotation of unsaturated oil sample molecules with low relaxation times.The highlight of the current dielectric spectroscopic study is that it conclusively divides the nine vegetable oil samples into two groups based on the dielectric property of relaxation time. The vegetable oil samples with higher relaxation times were measured in ps [soyabean oil (398.5), groundnut oil (412.5), flaxseed oil (318.4), and castor oil (305.3)] and the oil samples with lower relaxation times [safflower oil (37.91), sunflower oil (30.6), walnut oil (22.4) and sesame oil (38.4)] and correlate this dielectric aspect with the aspect extent of the presence of oleic acid: C18H34O2, linoleic acid: C18H32O2, linolenic acid: C18H30O2 and ricinoleic acid C18H34O3 alongside the percentage of unsaturation present in the fatty acid profile of each sample. Saturated fatty profile of coconut oil (percentage of saturation 82.5) with low relaxation time (41.8) ps and its concurrency concerning the extent of percentage presence of lauric acid C12H24O2 (52 ps) myristic acid: C14H28O2 (21 ps) is also correlated. The current dielectric spectroscopic study further highlights and compares the variances of dielectric permittivity of the nine vegetable oils samples with the percentage of unsaturation /saturation to infer upon the correlation with the fatty acid profile of these oil samples.

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ Tint ≤ 30 °C and 50 % ≤ Hint ≤ 70 % respectively. On the other hand, the control signals uf and uh applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ uf ≤ 4.3 Volts and 0 ≤ uh ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

Rural energy plays an important role in realizing the goals of “carbon peak” and “carbon neutrality” in China. In this paper, the countryside was regarded as the research object, and the rural energy internet was constructed to study the impact of rural energy development on rural carbon emissions. The most advanced energy and informative technologies in the development of rural energy were introduced from three perspectives, including rural living, rural planting and rural breeding. The benefits of rural energy internet in practical application, including energy and carbon benefits, were presented through three application cases. In general, a low-carbon, digital and intelligent rural energy will be developed, and the goals of “carbon peak” and “carbon neutrality” will be achieved by constructing and applying of rural energy internet in China.

The lack of information creates problems for Colombian small-scale farmers, as it impedes them from selling at fair prices and knowing efficient production techniques. Around the world, many technological interventions have proven helpful in reducing information asymmetries. Therefore, we proposed a technological scheme based on a genetic algorithm and a natural language processor (NLP) that enables producers to obtain knowledge through information processing. Also, we ran fieldwork in twenty municipalities and a survey among 500 Colombian cocoa small-scale farmers in different regions in Colombia. This fieldwork helps us determine small-scale farmers' necessities, market conditions, and the relevance of an Artificial Intelligence (AI) tool. The results have shown that AI methodologies could improve the economic conditions of small farmers by providing access to information on prices, weather, and production techniques. The fieldwork evidence that a technological tool is a good option only if there are dynamic trade cycles. AI tools could transmit and process information to become producers' knowledge and help them evolve into collective strategies. The methodology, which combines genetic algorithms, NLP, and fieldwork for cocoa farming, is a novelty that contributes to information asymmetry reduction. We contributed to the literature about adopting AI tools to develop cocoa small-scale farming better.

With the vigorous development of intelligence agriculture, the progress of automated large-scale and intensive pig farming has accelerated significantly. As a biological feature, the pig face has important research significance for precise breeding of pigs and traceability of health. In the management of live pigs, many managers adopt traditional methods, including color marking and RFID identification, but there will be problems such as off-label, mixed-label and waste of manpower. This work proposes a non-invasive way to study the identification of multiple individuals in pigs. The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3, a popular lightweight network. Then depth-wise separable convolution was adopted in YOLOv4′s feature extraction network SPP and PANet to further reduce network parameters. Moreover, CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight. The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features, so as to improve the overall model effect. Finally, an improved MobileNetv3-YOLOv4-PACNet (M-YOLOv4-C) network model was proposed to identify individual sows. The mAP were 98.15 %, the detection speed FPS were 106.3frames/s, and the model parameter size was only 44.74 MB, which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight, fast and accurate manner. This model will provide model support for subsequent pig behavior recognition and posture analysis.

Detection and counting of abalones is one of key technologies of abalones breeding density estimation. The abalones in the breeding stage are small in size, densely distributed, and occluded between individuals, so the existing object detection algorithms have low precision for detecting the abalones in the breeding stage. To solve this problem, a detection and counting method of juvenile abalones based on improved SSD network is proposed in this research. The innovation points of this method are: Firstly, the multi-layer feature dynamic fusion method is proposed to obtain more color and texture information and improve detection precision of juvenile abalones with small size; secondly, the multi-scale attention feature extraction method is proposed to highlight shape and edge feature information of juvenile abalones and increase detection precision of juvenile abalones with dense distribution and individual coverage; finally, the loss feedback training method is used to increase the diversity of data and the pixels of juvenile abalones in the images to get the even higher detection precision of juvenile abalones with small size. The experimental results show that the AP@0.5 value, AP@0.7 value and AP@0.75 value of the detection results of the proposed method are 91.14%, 89.90% and 80.14%, respectively. The precision and recall rates of the counting results are 99.59% and 97.74%, respectively, which are superior to the counting results of SSD, FSSD, MutualGuide, EfficientDet and VarifocalNet models. The proposed method can provide support for real-time monitoring of aquaculture density for juvenile abalones.

Plant growth monitoring techniques are of great interest to agricultural engineering. The interaction between root and soil water is one important plant response to environmental variations. This paper aims to develop a new method to estimate plant biological response using root-soil water interaction. It provides a case study on moisture transfer at the boundary area of a soil water retention zone (SWRZ). We produced a SWRZ around growing roots of a cultivated tomato plant in homogenous dried soil using water-saving drip irrigation. The irrigation was designed to supply moisture only in the root zone to meet the minimum need of plant growth. High-resolution soil moisture sensors were used to detect moisture transfer at the boundary area of the SWRZ. We applied frequency analysis to the acquired vibration spectrum using filtering and Fast Fourier Transform (FFT) in order to investigate the frequency content at each sensor location. Distinct frequencies of moisture vibration were identified at the boundary area of the SWRZ which indicated water transfer to the roots caused by plant water absorption. A mechanical vibration model was proposed to describe this phenomenon. The pinpoint irrigation to the root zone in the water-saving cultivation method enabled a well-structured spherical root system to form via hydrotropism. This enabled a simple method to analyze moisture transfer based on a mechanical vibration model. The results suggest a new method to estimate plant biological response by studying root-soil water interaction.

Collections of biological specimens are essential in entomology laboratories for scientific knowledge and the characterization of natural varieties. It is vital to liberate useful information from physical collections by digitizing specimens, allowing them to be shared, examined, annotated, and compared more readily. As a result, current research has concentrated on developing 3D modeling machine systems to digitize insect specimens. Despite many great outcomes, these systems have certain drawbacks. In this research, a new scanning machine is proposed for creating 3D virtual models of insects. Our method has overcome certain previous constraints by aiding in the automation of the entire imaging process at a low cost, lowering shooting time, and generating 3D models with accurate color, high resolution, and high accuracy of insect samples with small sizes and complicated structures. Because of its ease of installation and modification, our system may be expanded and utilized in a variety of settings and areas.

UAVs (Unmanned Aerial Vehicles) have become increasingly popular in the agricultural sector, promoting and enabling the application of aerial image monitoring in both the scientific and business contexts. Images captured by UAVs are fundamental for precision farming practices. They enable us do a better crop planning, input estimates, early identification and correction of sowing failures, more efficient irrigation systems, among other tasks. Since all these activities deal with low or medium altitude images, automated identification of crop lines plays a crucial role improving these tasks. We address the problem of detecting and segmenting crop lines. We use a Convolutional Neural Network to segment the images, labeling their regions in crop lines or unplanted soil. We also evaluated three traditional semantic networks: U-Net, LinkNet, and PSPNet. We compared each network in four segmentation datasets provided by an expert. We also assessed whether the network’s output requires a post-processing step to improve the segmentation. Results demonstrate the efficiency and feasibility of these networks in the proposed task.

Abstract Cotton is a major cash crop in the agriculture of Bangladesh, primarily fueling its substantial textile industry. Although local production is limited, cotton has the potential to bolster the country’s economic growth by supporting local economies and improving agricultural sustainability. An in-depth sustainability assessment of this integral crop is essential to ensure its long-term economic and environmental viability. This study aims to evaluate the sustainability of cotton production in Bangladesh and suggest strategic enhancements and policy measures to improve its environmental, economic, and social aspects. Data were collected from 60 farmers in two villages of Jashore district using a simple random sampling technique. A combination of descriptive, mathematical and statistical techniques was used to analyze the data. The findings highlighted a profitable benefit–cost ratio of 1.88 for cotton production. Positive impacts on cotton profitability were notably linked to the costs of labor, land preparation, and insecticides and pesticides. Despite this, 60% of farmers faced substantial challenges, including financial barriers, limited access to modern agricultural technologies, and a lack of training in sustainable practices. Economic constraints were a major barrier, although the environmental benefits of sustainable practices were widely acknowledged. To address these issues, the study recommends optimizing inputs like labor, land preparation, and pest management, and advocates for policy actions such as subsidies, tax incentives, and enhanced training programs to support the widespread adoption of sustainable practices across Bangladesh.

Abstract The aims of the present stud were to optimize fermentation parameters of seedless wampee wine using response surface methodology (RSM) and evaluate the changes in flavor metabolites during fermentation. Seedless wampee wine of optimal sensory quality was produced using an inoculum concentration of 0.6%, initial sugar levels of 200 g/L, a fermentation temperature of 22 °C, and a fermentation period of 9 days. Then the flavor compound profiles (amino acids, organic acids and volatile aroma compounds) of seedless wampee wine during the fermentation under optimal conditions were analyzed using high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometr (GC-MS). The main fermented phase of fermentation resulted in fluctuations in both total amino acids and organic acids, with stabilization occurring later on. A total of 54 volatile components, including esters, alcohols, terpenes, and acids, were putatively identified. Terpenes were the primary drivers of the flavor characteristics of seedless wampee. The rise of esters and decline of terpenes have the potential to significantly alter the flavor of wine during fermentation. These results would contribute to the further development of seedless wampee wine. Graphical Abstract

Abstract Biochar is a nature-based solution for sustainable agriculture but its potential adoption in some parts of sub-Saharan Africa is still minimal. In this study, we evaluated the agronomic potential of maize stover biochar in cowpea-maize sequential cropping in Uganda under field conditions. The treatments included; the common farmer practice of no inorganic fertilizer and no biochar (CTR), inorganic fertilizer (F), 10 t ha−1 biochar (B10), 40 t ha−1 biochar (B40), 10 t ha−1 biochar + inorganic Fertilizer (FB10), and 40 t ha−1 biochar + inorganic Fertilizer (FB40), arranged in a randomized complete block design (RCBD) with three replications. The results showed that cowpea seed yield was not significantly affected by biochar and fertilizer application but the haulm yield was significantly improved only in FB40 treatment. Maize grain and stover yield was significantly improved only in the FB40 treatment but biochar showed a high potential to also improve yield even without inorganic fertilizer. The potential for biochar to improve maize yield either in the presence or absence of fertilizers could be attributed to the residual soil fertility from cowpeas. In both seasons, biochar significantly improved soil pH, EC, SOC, total N, available P, exchangeable K and Ca, irrespective of fertilizer application. However, exchangeable Mg did not significantly vary among the treatments. This study further revealed that in cowpea-maize rotation, optimum yield could also be possible with sole biochar application. Therefore, instead of burning the maize stovers after harvest, farmers should convert the residues into biochar and return it to the soil so as to achieve sustainable food systems. Graphical Abstract

Abstract Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p

Abstract The domestication of agriculture is widely recognized as one of the most crucial technological adaptations for the transition of humanity from hunter-and-gatherer groups into early city-states and ultimately, complex civilizations. As humankind sets forth to permanently establish itself on the Moon and use it as a testing ground to colonize other worlds, like Mars, agriculture will again play a pivotal role. In this case, the development of sustainable crop production systems capable of succeeding in these harsh environments becomes vital to the success of our star-faring journey. Over decades, studies varying in species and approaches have been conducted in microgravity, testing the limits of plants and various growth systems, to better understand how Earth-based agriculture could be translated into environmental conditions and therefore evolutionary pressures beyond what life on our planet has known. While we have passed several significant milestones, we are still far from the goal of a sustainable agricultural system beyond our planet Regolith-based agriculture (RBA) should be a component of sustainable agriculture solutions beyond Earth, one which can also provide insight into plant growth in poor soils across our own world. However, RBA studies are in their infancy and, like any other new field, need an established set of parameters to be followed by the RBA community so the generated data can be standardized and validated. Here, we provide an extensive multi-disciplinary review of the state of RBA, outline important knowledge gaps, and propose a set of standardized methods and benchmarks for regolith simulant development and selection as well as plant, microbe, and plant-microbe interaction studies conducted in lunar and Martian regolith. Our goal is to spur dialog within the RBA community on proper regolith simulant selection, experimental design, and reporting. Our methods are divided into complexity tiers, providing a clear path for even the simplest experiments to contribute to the bulk of the knowledge that will shape the future of RBA science and see it mature as an integrated part of sustainable off-world agriculture.

Abstract The China-Pakistan Economic Corridor (CPEC) is a cornerstone of China’s Belt and Road Initiative (BRI). It aims to enhance regional trade and economic expansion in Pakistan. We adopted systematic review approach to investigate the agricultural trade narrative of the CPEC and identify future research avenues. Our study uses the Web of Sciences and Scopus database to extract the relevant literature. We executed the search query for 2013–2023. We followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement to identify the literature. The final analysis includes only 14 articles. We found that the literature mainly focuses on three topics, Gwadar port, road infrastructure, and agricultural complementarity and competitiveness. The review reveals the significant potential of CPEC on the agricultural trade of Pakistan. Based on the review, we point to avenues that could be considered in future research work to fully exploit the potential of CPEC in the agriculture sector of Pakistan.

Abstract Ultraviolet-B (UVB) radiation-induced photoaging of the skin is characterized by amplified expression of matrix metalloproteinase-1 (MMP-1) and reduced collagen fibers, both of which contribute to skin wrinkle formation. Edible natural products can protect against skin photoaging. Here, we investigate the protective effect of Dolichos lablab Linne (DLL) water extract against UVB radiation-prompted skin damage and attempt to uncover its fundamental mechanisms in human keratinocytes (HaCaT) and HR-1 hairless mouse. We found DLL extract rescued the reduction in cell viability associated with UVB exposure without any associated cytotoxic effects. It also protected against skin photoaging by inhibiting mitogen-activating protein kinase (MAPK) signaling, thereby preventing the UVB-associated increase in MMP-1 and -9 expression. DLL extract also increased the expression of both superoxide dismutase 1 (SOD1) and catalase (CAT). We identified chikusetsusaponin IVa, soyasaponin Bb, and sandosaponin A as bioactive components of DLL. Although we have not yet identified the mechanisms by which these compounds reduce the effects of photoaging, we have demonstrated that chikusetsusaponin IVa, soyasaponin Bb, and sandosaponin A reduce MMP-1, MMP-9, p–c-Fos, and p–c-Jun expression, while also avoiding any cytotoxicity. We found oral administration of DLL extract effectively alleviated dorsal epidermal thickening and skin dehydration in HR-1 hairless mouse visible to UVB. DLL extract also prevents UVB-induced activation of the MAPK/AP-1 signaling pathway, thereby reducing the expression of MMPs in dorsal mouse skin. Our results indicate that chikusetsusaponin IVa, soyasaponin Bb, and sandosaponin A are bioavailable components of DLL extract that can reduce UVB-induced skin damage via MMPs by deactivating the MAPK/AP-1 signaling pathway. These findings suggest DLL extract can be used as a skin anti-photoaging agent.

Society is increasingly aware of the connection between the health of the land, of animals and of humans. Visions of ‘foodscapes’ and ‘healthscapes’ are eclipsing the conventional view of landscapes focussed solely on production. Livestock production farming systems must co-evolve with this thinking. Lincoln University has designed and is implementing the Integral Health Dairy Farm (IHDF) to test and communicate these new and transformational systems views. Its objective is to innovate, to demonstrate and to manage a tangible transition from current practices to a system designed to enhance health, from the ground up. This includes measured improvements in soil, plant, animal, human and community health. In this ‘comment’, we focus on applied scientific integration of the ‘One health approach’ into agricultural systems of livestock, presenting our initial design and prototyping processes, as well as how it continues as the project moves from the drawing board to implementation, benefiting from a growing network of supporters and collaborators.

Abstract In this study, we investigated the metabolic behavior of the engineered Clostridium acetobutylicum ATCC 824 (pCD07239) strain during the acidogenic phase under varying glucose concentrations and pH conditions. Unlike the wild-type C. acetobutylicum ATCC 824, the engineered strain exhibited negligible butyrate production and simultaneous butanol production during the acidogenic phase under limited glucose condition of 25 g/L. Specifically, batch fermentations of the engineered strain with 25 g/L glucose at a pH of around 5.0 (initially uncontrolled) demonstrated butanol production of 2.99 g/L, while butyrate remained below 0.30 g/L. Separately, in batch fermentations at pH 6.0 with 90 g/L glucose, acetate production nearly doubled compared to fermentations at pH 5.0 with the same glucose concentrations, reaching a maximum concentration of 11.43 g/L, while butyrate production remained relatively low at 4.04 g/L. Under these pH 6.0 and 90 g/L glucose conditions, butanol production reached 9.86 g/L. These findings indicate that C. acetobutylicum ATCC 824 (pCD07239) maintained low butyrate production, even under conditions favoring acidogenesis, and consistently produced butanol. Additionally, the negligible production of acetone at pH 6.0 further indicates that the traditional phase transition was not prominent, suggesting altered regulation mechanisms in the engineered strain. These findings highlight C. acetobutylicum ATCC 824 (pCD07239) strain’s unique metabolic profile and its potential for efficient biobutanol production under diverse conditions.

This study was conducted to investigate the effects of salicylic acid and harvest date on the yield and biochemical and physiological characteristics of peanuts in different humidity conditions, which is the novelty of this research. For this purpose, a split-plot factorial design during the 2018 and 2019 cropping seasons was conducted based on a randomized complete block design with three replications at the experimental field of the Agricultural and Natural Resources Research and Education Center of Guilan Province, Astara, Iran. Humidity regimes included rain-fed conditions (no irrigation), irrigation at the 10% flowering stage, 10% pod formation stage, and 10% flowering stage +10% pod formation stage in the main plots, and three salicylic acid levels (100, 200, and 300 µmol/l), and harvest dates (August 22, September 6, and September 21), were placed in subplots. Drought stress reduced the Chlorophyll Index, relative water content, seed, and pod yield but increased proline, Antioxidant enzyme activity, soluble sugar content, and H2O2 content. Conversely, Supplementary irrigation decreased proline, antioxidant enzyme activity, soluble sugar content, and H2O2 content, which increased the Chlorophyll Index, relative water content, seed, and pod yield. Also, salicylic acid foliar application enhanced guaiacol peroxidase, polyphenol oxidase, and ascorbate peroxidase antioxidant enzyme activity, mitigating the undesirable impacts of water deficiency. The maximum pod and seed production was obtained in all humidity conditions with 300 µmol/l salicylic acid. Under Humidity stress conditions, if water resources are available, two supplementary irrigations at 10% flowering and 10% pod formation stage are recommended. Also, for humid climates such as Astara and similar regions, in terms of climate, with limited rainfall in June and July (rainfall less than 10 mm during flowering and podding stages), one supplementary irrigation at 10% pod formation stage is recommended.

Thinning is an expensive and time-consuming management practice used in peach orchards to improve resource distribution among plants and improve production quality. Determining the right time and intensity for thinning is challenging and involves expertise. Furthermore, it generally consumes many hours of work, which makes, in some cases, unfeasible to analyze an entire orchard. For this reason, information that can assist in making making decisions about thinning can improve the cost–benefit ratio of the technique. To mitigate these problems, an electronic nose system, the e-nose, that explores the relationship between the smell of peach trees outdoors and the different growth phases was developed. Twenty-two composed volatile samples were collected from around peach trees (open environment) during its reproductive period (around 39 days) and five supervised machine learning classification algorithms (k-nearest neighbors (KNN), multilayer perceptron (MLP), random forest (RF), logistic regression (LR), and support vector machine (SMV)) were used to analyze the data, to evaluate the possibility of estimating phenological stages from odor environment. The result showed that all models achieved a balanced accuracy greater than 97.5%. As a secondary contribution, the importance of sensors was also analyzed for this application, and a combination of three sensors achieved a classification rate of 100% with the KNN classifier. The e-nose system was successful in distinguishing between petal drop, initial fruit formation, advanced fruit formation, and formed fruit. These results demonstrate the potential of using an electronic nose in a remote system to assist in decision-making in orchard practices as thinning.

The increasing global population and growing demand for food and mineral fertilizers underscore the urgent need to enhance fertilizer efficiency. This imperative emphasizes the importance of sustainable fertilizer utilization while mitigating environmental impacts, particularly in addressing agricultural water pollution. Excessive fertilizer use contributes significantly to water contamination and food shortages worldwide. In 2018, food shortages were reported in many nations, including the United States (2.3%), Canada (4.6%), the United Kingdom (8.2%), Germany (2.6%), Japan (2.9%), Ethiopia (23.4%), Ivory Coast (22.4%), Bangladesh (12.7%), Pakistan (17.2%), Haiti (45.6%), and India (14.3%). Moreover, agricultural activities, particularly the use of mineral fertilizers, are major contributors to greenhouse gas emissions. Inefficient fertilizer practices lead to economic losses, environmental degradation, and food insecurity. Studies reveal that in sub-Saharan Africa, farmers receive only about $0.50 in increased productivity for every dollar spent on fertilizer due to inefficiencies. The economic cost of nutrient pollution in the European Union is estimated to range between €7 billion and €10 billion annually. Effective strategies like precision nutrient management, best practices, and innovative technologies optimize fertilizer efficiency and support agricultural sustainability. Besides, promising methods include the combined use of organic and inorganic fertilizers, the application of remote sensing and geographical information system technologies, and the implementation of biological approaches to enhance nutrient management. Moreover, monitoring and evaluation are essential for assessing strategy effectiveness, guiding decision-making, and taking corrective actions. Hence, this review aims to address strategies for improving fertilizer efficiency, sustainable agriculture practices, and addressing food security and environmental concerns related to fertilizer use comprehensively.

Dairy cattle production plays a great role in food security and generates income, draught power, and manure input for crop production. The present review focuses on major reproductive performance indicators; age at first service (AFS), age at first calving (AFC), calving interval (CI), days open (DO), and number of services pre conception (NSPC) of improved dairy cattle in Ethiopia. The overall mean values of AFS, AFC, CI, DO, and NSPC were 27.8 ± 5, 37 ± 4.9, 442.7 ± 80.5, 126 ± 40.27 days and 1.57 ± 0.23 numbers, respectively. The review showed the AFS of exotic dairy cattle breeds as 30.5 ± 2.6 for pure exotic, 26.8 ± 3.8 for 75–100%, 25.04 ± 4.5 for 50–75%, 26.7 ± 7 for 50% and 28.1 ± 5.7 days for

Although edible insects (EI) have been consumed for ages in many countries around the world, in most European countries, this is not a traditional practice. Still, it has been encouraged as a sustainable source of animal protein besides other nutrients. The Food and Agricultural Organization and the United Nations, recommend consuming EI as a way to contribute to the sustainable development goals. Hence, this research investigated how participants from Portugal and Romania perceive EI, and how these can be useful as a source of nutrients and other benefits for human health, as well as possible risks. For that, it was undertaken a questionnaire survey, with data collected online in the two countries. For the analysis of data, basis statistics and chi-square tests were used. Results showed significant differences between Portuguese and Romanian participants in six out of ten questions, namely regarding safety (p < 0.001), use in traditional medicine (p < 0.001), possible infection with parasites (p = 0.021), of pesticides (p = 0.031), their usage as therapeutics (p = 0.001) and being potential sources of allergens (p < 0.001) and aflatoxins (p < 0.001). Also, differences were tested for other sociodemographic variables. The results showed significant differences according to age for only three questions, gender for only two questions, and Education for most of the questions, i.e., six out of the ten. These results highlighted the greater influence of variables like country of origin and education in the perception of the effects of EI in human health.

The aim of the work was to test the level of daylight conditions and the heat load of animals in the summer season in two identically oriented dairy barns with different construction and material solutions. Measurements were carried out in the same farm and at the same time. A classic massive building – reconstructed barn (RB) for 158 dairy cows with a volume of 34.3 m3 per animal supplemented in summer by motor ventilation (522.15 m3 per animal, total 82,500 m3·h−1) had an indoor daylight factor DF below the limit DFavg, int = 0.21% (against the recommended DF = 1% for housing dairy cows). In the external feeding alley, which was screened by a shelter and an adjacent building, lighting conditions were compliant with DFavg, ext = 4.02%. However, the level of heat load as measured by indices was above the limit (THIavg, int = 84.65) despite the motor ventilation used; in the outdoor feeder THIavg, ext = 81.63, against the recommended stress-free level THI = 72. Similarly, according to the ETIC assessment, the results were deficient ETICavg, int = 26.16 and in the outdoor feed ETICavg, ext = 25.24 against the recommended level of ETIC = 20. The second building, a new barn (NB) for 444 dairy cows was a new large-cubicle indoor structure with 82.53 m3 per animal, with a translucent roof (37.87% of the total 4,927.6 m2 was of translucent polycarbonate triple-pipe roofing). This amount of translucent elements together with large window openings of a total area of 879.6 m2 was also reflected in the values of the daylight factor DFavg, int = 7.59%. The level of heat load was not significantly lower compared to a massive RB with motor ventilation over a double row of bunks (fans with a total capacity of 82,500 m3·h−1). In NB, no animal cooling features were activated, but technically and spatially they were easily solvable (whether economical ventilation systems with laminar flow, precisely controlled sprinkling of animals, or another combination of evaporative and flow solutions). These efficient types of cooling would be almost impossible to deploy in RB or only with a special renovation of the building, which would be comprehensively costly.

The aim of the work was to evaluate the influence of changes of technical working parameters of a field sprayer on changes of the coefficient of variation for double stream atomisers. The technical parameters were the height and the inclination angle of the sprayer boom and spraying pressure. In the studies, a Drift Guard TwinJet Flat Spray nozzle (DGTJ60 11003 VS) was used. It generated two flat fans of liquid at the spray angle of 110°, directed forward and backward in relation to the direction of sprayer movement. It has a 60° angle between its two fans. The measurement of the coefficient of variation of transverse liquid distribution was done using the Hardi Spray Scanner patternator. The obtained results indicate that the height of field boom setup, the angle of inclination, and their interaction had the strongest influence on the value of the coefficient of variation of transverse liquid distribution for the field boom equipped with the DGTJ60 11003VS nozzle. Field boom setup during the spraying at the height of 0.5–0.6 m, keeping field boom inclination from 0° to 2°, and using the liquid pressure of 0.4 MPa allowed for gaining the lowest values of the coefficient from 5.03% to 6.53%.

On low-lying soils with a high clay content and poor water conductivity, excess water regularly appears after almost every rainy winter causing soil degradation, which leads to yield failures and/or losses. To outlet the harmful excess surface, waters needs appropriate ameliorative and agrotechnical operations. Among them, scraping represents a suitable intervention into soil structure involving the complete conversion of the micro-topography of the land. The humus content of the soil is a determining factor in terms of soil fertility; therefore, it must be preserved even when drastic tillage operations are carried out on a plot. Our research aimed to assess the effect of scraping on the humus content of the soil on three plots with a heavy textured Vertisol endangered by excess waters. Scraping was used on these plots to create a homogeneous topography and surface with a slight slope to get rid of excess water when it appears. It was established that scraping did not cause humus loss on a plot scale, and it resulted in an evener organic matter distribution, hence a potentially more homogenous yield within the ameliorated plots.

A possible way to increase the operating speed and field capacity of a cultivator-fertiliser is to modify its configuration scheme. According to our hypothesis, placing the fertiliser hopper directly on the tractor frame could reduce the traction resistance of the cultivator-fertiliser and consequently increase its operating speed and field capacity. Validating this hypothesis requires a comparative evaluation of the existing configuration schemes for cultivator-fertilisers. However, there are currently no such comprehensive studies available. This research aims to develop a mathematical model for conducting a comparative theoretical evaluation of various configuration schemes for cultivator-fertilisers. The development of this model incorporated principles from classical and agricultural mechanics. We developed a mathematical model and conducted a theoretical evaluation of existing configuration schemes for cultivator-fertilisers, such as mounting the hopper on a separate trailed chassis, mounting the hopper and tillage working bodies on one frame, and mounting the hopper directly on the tractor frame. The working width of the cultivator-fertiliser was constant and amounted to 6.2 m. The effective engine power of the tractor was taken to be 220 × 103 W. It was found that placing the fertiliser hopper on the tractor frame maximises the field capacity to 3.9 ha∙h−1. The increase in field capacity was 8.9–16.0%. In this case, the fertiliser hopper volume should be 1.89 m3 and travel speed 8.45 km∙h−1.

In accordance with EU legislation, the Czech Republic has been progressively implementing stringent regulations for the landfilling of municipal waste. The main reason is to facilitate the transition to circular economy and maximise material and energy utilisation. The aim of this study was to provide insights on biodegradable waste management in Czechia and to monitor changes related to landfilling at two distinct landfill sites in the Czech Republic fulfilling new legislation requirements. The analysis, underpinned by monitoring data on the amount and composition of waste landfilled from 2014 to 2023, has delineated a decline in the proportion of biodegradable waste. This trend aligns with that observed in other European countries. Furthermore, reduced energy production with a decreasing proportion of biodegradable waste in municipal solid waste was confirmed. The study points to the need for improved biodegradable waste separation, and it stands as a case study in the formulation of biodegradable waste strategies.

The estimation of machine‘s centre of gravity (COG) dislocation is presented in this article. Various types of agricultural tractors‘ parameters were analysed. Their parameters such as track width in the range of 〈1222 mm, 1900 mm〉 and COG height in the range of 〈830 mm, 1340 mm〉 are known from the reports of OECD tests. The static stability of the machine is defined by the critical angle of labile equilibrium or critical overturning angle in static disposition, which is determined from measurement on the tilt table. The critical overturning angle can be calculated from the known parameters of COG dislocation. The critical overturning angle for each analysed machine can be evaluated from the obtained dataset. The mean and standard deviation of parameters were used as input parameters into Monte Carlo simulation (MCS). The MCS method was used for the estimation of the critical overturning angle and height of COG for 2500 data. Simulation was performed using a Mathcad® Prime 9.0 software. The basic statistical analysis was performed using a Spyder software in the Python language. The polynomial approximation equation was set up, and then COG heights were calculated. The critical overturning angle was calculated from the real and simulated parameters. The functions of the critical overturning angle were compared, and the fit of approximation was evaluated by the R2 parameter. The designed model was verified by the F-distribution test.

The aim of this paper is to investigate the possibility of using two different methods together to detect more accurately and to evaluate thermal defects in the envelope of agricultural buildings. The structure of heated agricultural buildings is significantly influenced by the characteristics of the indoor environment (aggressive gaseous and liquid substances, high humidity, higher heating temperatures, etc.). In this study, research has been conducted that synergistically combines two methods for the evaluation of thermal defects, where the structure is evaluated using infrared thermography and at the same time described as a numerical model for a more detailed identification of envelope defects and thermal bridges. The study confirmed that in agricultural building structures, thermal defects are most commonly found in the areas of window lintels, ceiling and bracing structures, window and door joints, and foundation and floor joints, while the proposed combination of the two methods for assessing thermal defects in structures demonstrated superior performance in terms of accuracy and detail of results obtained compared to the standard partial use of only one of these methods.

This study aims to investigate the effects of manure and vermicompost in terms of soil elasticity modulus changes in loam soil. The uniaxial compression test measures the compressive strength of a soil cylinder without any lateral load. The modulus of elasticity was determined by plotting the strain-stress diagrams and calculating the slope of these diagrams. The increase in moisture and organic matter content significantly improved soil flexibility and decreased its elasticity modulus. It was found that adding animal manure had a greater effect on the flexibility of the soil as compared to vermicompost, while the elasticity modulus of the samples containing animal manure was lower. The interaction effect of moisture and the organic matter content indicated that the increase in humidity was more effective in the soil containing vermicompost as compared to the soil containing manure, such that when the manure rate was around 19%, the moisture increase had no significant impact on the elasticity modulus. It was found that adding organic matter was more effective at the low moisture level of 15.5% than at higher moistures where the effect of adding organic matter did not affect the soil elasticity modulus decrement as strongly.

Abstract The increasing demand for sustainable agricultural practices necessitates innovative solutions for soil fertilization. This study aimed to assess the viability of using limestone mining co-products from the Irati and Corumbataí Formations as multi-nutrient sources for Glycine max cultivation. The experiment utilized a double factorial design (7 × 2) with five replications, randomly arranged and placed in pots within a greenhouse. The factors examined were the type of co-product (sedimentary rocks from the Irati Formation: rhythmite and bituminous shale; and the Corumbataí Formation: clayey siltstone) and the soil type (Haplic Arenosol and Rhodic Ferralsol). Soil ion content was assessed at 21 and 140 days after treatment application. Each pot housed four G. max plants, with leaf harvest occurring after 42 days of growth. Macronutrient content, leaf production, and soil ion availability were determined thereafter. Overall, co-product application led to increased soil pH and availability of essential ions, including K+, Mg2+, Ca2+, P, S-SO4 2−, and Si. At 140 days, rhythmite was notably effective in increasing pH, Ca2+, and Mg2+ in Haplic Arenosol, with respective increases of 22%, 224%, and 696%. Bituminous shale, on the other hand, significantly increased S-SO4 2− content by 67% and 426% in Rhodic Ferralsol and Haplic Arenosol, respectively. Moreover, the application of co-products resulted in enhanced accumulation of K, Ca, Mg, and S in G. max plants, alongside increased leaf production. In conclusion, the utilization of mining co-products positively influenced soil and plant chemical attributes, particularly enhancing leaf production in G. max. These findings highlight the potential of co-products as environmentally friendly solutions for soil fertilization, supporting sustainable agricultural practices. This research highlights a significant step towards sustainable agriculture by recycling mining waste into valuable soil amendments, which can improve crop yields and reduce the environmental impact of both mining and agricultural activities. Graphical Abstract

Abstract The North Western Himalayan region, particularly the Kashmir valley, harbors a rich diversity of rice landraces, yet systematic studies on climate resilience are lacking. The present study was aimed at investigating the root traits and their interplay with shoot morphological and physiological traits under drought stress using seven indica and three japonica rice varieties released by SKUAST-Kashmir and their possible role in adaptive capacity to different environments. Significant variability was observed in root and shoot traits across the varieties. Root traits exhibited a wide phenotypic range, including rooting depth, fresh and dry weight, diameter, length, volume, surface area, and length density. Associations between root and shoot traits were identified, with notable correlations such as shoot weight with root shoot ratio, shoot length with root dry weight, diameter, and surface area, and tiller number with various root traits. Additionally, physiological traits like canopy temperature depression showed significant correlations with root depth and surface area. Principal component analysis (PCA) analysis grouped japonica type varieties together with a notable outlier, Chenab, due to its superior root traits among indica varieties. These findings emphasize the substantial contribution of root traits to productivity and advocate for their integration into varietal development processes. In view of the increasing evidences in crop plants about their role of root traits in defining plant productivity and reproductive fitness, study of root traits could provide valuable insights into the patterns of crop adaptation to diverse areas and growing environments.

Abstract This study focused on identifying and characterizing 1,3-diphenylpropane derivatives from flavonoids that inhibit human indoleamine 2,3-dioxygenase 1 (hIDO1) enzymes, which play a role in immune regulation and are associated with various diseases. A series of isolated metabolites (1–7) demonstrated modest to high inhibition of hIDO1, with binding degree values ranging from 26.31 to 72.17%. In particular, during a target-based screening of natural products using hIDO1, kazinol J (6, a 1,3-diphenylpropane derivative) was found to potently inhibit hIDO1, with a binding degree of 72.17% at 1 ppm. Kazinol J (6) showed concentration-dependent and mixed inhibition kinetics and achieved slow and time-dependent inhibition of hIDO1. Additionally, docking simulations were performed to evaluate the inhibitory potential and binding interactions of the compounds with hIDO1. These findings suggest that these 1,3-diphenylpropane derivatives can serve as therapeutic agents for conditions involving hIDO1 dysregulation, such as cancer, autoimmune disorders, and infectious diseases.

Abstract Acokanthera schimperi is a medicinal plant traditionally used for the treatment of wounds, scabies, and malaria. Rhus glutinosa has been also utilized for the management of ectoparasites and hemorrhoids. Silica gel column chromatography separation of CH2Cl2/MeOH (1:1) extract root of A. schimperi afforded oleic acid (1), lupeol (2), dihydroferulic acid (3), acovenosigenin A- 3-O-α-L-rhamnopyranoside (4) and sucrose (5) whereas CH2Cl2/ MeOH (1:1) and MeOH roots extracts of R. glutinosa afforded β-sitosterol (6), (E)-5-(heptadec-14-en-1-yl)-4,5-dihydroxycyclohex-2-enone (7), methyl gallate (8), and gallic acid (9). The structures of the compounds were established using spectroscopic (1D and 2D NMR) and FT-IR techniques. Disc diffusin and DPPH assay were used, respectively, to evaluate the antibacterial and antioxidant potential of the extracts and isolated compounds. MeOH extract root of A. schimperi showed a modest antibacterial effect against E.coli with an inhibition zone (ZI) of 16 ± 0.0 mm compared to ciprofloxacin (ZI of 27.0 ± 0.0 mm). CH2Cl2/MeOH (1:1) and MeOH root extracts of R. glutinosa showed maximum activity against S. aureus with ZI of 17.3 ± 0.04 and 18.0 ± 0.0 mm, respectively. At 5 mg/mL, the highest activity was noted against S. aureus by 8 with ZI of 18.6 ± 0.08 mm. Dihydroferulic acid (3), methyl gallate (8), and gallic acid (9) displayed potent scavenging of DPPH radical with respective IC50 of 10.66, 7.48, and 6.08 µg/mL, compared with ascorbic acid (IC50 of 5.83 µg/mL). Molecular docking results showed that lupeol (2) exhibited strong binding energy of -7.7 and − 10 kcal/mol towards PDB ID: 4F86 and PDB ID: 3T07, respectively, compared to ciprofloxacin (-6.5 and − 7.2 kcal/mole). Towards PDB ID: 1DNU receptor, compounds 3, 8, and 9 showed minimum binding energy of -5.1, -4.8, and − 4.9 kcal/mol, respectively, compared to ascorbic acid (-5.7 kcal/mol). The Swiss ADME prediction results indicated that compounds 2, 3, 8, and 9 obeyed the Lipinksi rule of five and Veber rule with 0 violations. The in vitro antibacterial and antioxidant results supported by in silico analysis indicated that compounds 2, 3, 8, and 9 can potentially be lead candidates for the treatment of pathogenic and free radical-induced disorders.

Crop species undergo artificial selection as a result of domestication under agricultural field conditions. However, there is limited information on the pattern of how domestication alters leaf photosynthesis, morphology, and biomass and its allocation. In this review, we firstly introduced the concept of crop domestication, provided clarity on crop domestication syndrome, and emphasized the significance in the conservation and re-utilization of wild crop resources. Next, we discussed the variation in crop biomass and yield using a compiled dataset comprised of 54 wild and cultivated species. We subsequently summarized the general shift in physiological traits including higher growth and photosynthetic rates, light use efficiency, leaf area, chlorophyll, and nitrogen content, which may be associated with greater biomass and yield during crop domestication. We ended by identifying what has been learned on how domestication optimized plant performance to produce today's crops, and by providing some examples of how this knowledge was being exploited and redirected to drive crop improvement in the near future. These general patterns following crop domestication present several implications for offering valuable insights into shaping future genetic engineering targets and improving agricultural management practices.

Improving grain yield (GY) is a major goal of durum wheat breeding programs, and a high yield needs to be attained while preserving grain quality. The current study evaluated the changes caused by a breeding program on the association between GY and grain protein content (PC) in Moroccan durum wheat. Field experiments with six leading varieties released over three decades were conducted for three growing seasons under two water regimes. GY and PC were largely controlled by environmental effects, including both the growing season and water regime. The year of release and growing season effects were the predominant sources of variation in total chlorophyll content (TCC). A significant increase in GY with the year of release was observed under irrigated conditions, whereas a significant increase in PC over time was noted under rainfed conditions. In addition, a negative correlation was revealed between GY and PC, which was significant only in the rainfed trials. Similarly, a significant and positive association was noted between PC and TCC within the rainfed trials, but not in the irrigated trials. Based on GGE (genotype main effect plus genotype ​× ​environment interaction) biplot analysis, the new varieties ‘Marouane’ and ‘Faraj’ were identified as desirable varieties in terms of GY and PC close to the ‘ideal genotype’. These results indicated that when grown under current irrigated conditions, new varieties improved by the Moroccan breeding program showed better GY performance than old varieties.

Photosynthesis represents the most important biological process on earth and generates food and energy for most living organisms. Increasing photosynthetic efficiency in crops is a feasible strategy to enhance grain yield. Canopy photosynthesis, the integral of photosynthesis of all photosynthetic tissues of an entire plant canopy, is intrinsically linked to biomass production and crop yield and is influenced by both photosynthetic efficiency at the tissue level and canopy architecture, which determines the light environment at that tissue. This review summarizes current methodologies for measuring photosynthesis via gas exchange, fluorescence, and reflectance spectrum at the field, canopy, and leaf levels. Gas exchange techniques include eddy covariance, canopy gas exchange chambers, and organ-level gas exchange methods, which can measure CO2 and H2O fluxes. Chlorophyll fluorescence methods include solar-induced fluorescence (SIF), laser-induced fluorescence transient (LIFT), pulse amplitude modulated (PAM) fluorescence, and light induced chlorophyll a fluorescence rise (OJIP transient), which can be used to evaluate photosynthetic efficiency and plant stress responses. One of the commonly used reflectance spectrum methods for studying photosynthesis is the hyperspectral reflectance technique, which can estimate photosynthesis-related traits. High-throughput crop photosynthesis phenotyping can be performed with different combinations of these techniques. This review aims to provide a one-stop resource for researchers working in plant physiology, agronomy, and environmental science and working on either crop management or genetic enhancement to address either food security or the response of plants to global climate change.

Crop production stands as a pivotal pillar of global food security, but its sustainability faces complex challenges from plant diseases, which pose a substantial threat to agricultural productivity. Climate change significantly alters the dynamics of plant pathogens, primarily through changes in temperature, humidity, and precipitation patterns, which can enhance the virulence and spread of various plant diseases. Indeed, the increased frequency of extreme weather events, which is a direct consequence of climate change, creates favorable conditions for outbreaks of plant diseases. As global temperatures rise, the geographic range of many plant pathogens is expanding, exposing new regions and species to diseases previously limited to warmer climates. Climate change not only affects the prevalence and severity of plant diseases but also influences the effectiveness of disease management strategies, necessitating adaptive approaches in agricultural practices. This review presents a thorough examination of the relationship between climate change and plant pathogens and carefully provides an analysis of the interplay between climatic shifts and disease dynamics. In addition to insights into the development of effective strategies for countering the adverse impacts of climate change on plant diseases, these insights hold significant promise for bolstering global crop production resilience against mounting environmental challenges.

Many efforts have been made to enhance rice photosynthesis for higher grain yields, although such knowledge has seldom been integrated into rice breeding programs. In this review, I first address the limitations and challenges of the theory that yield is controlled by photosynthesis, a concept rooted in the fact that carbon forms a significant part of plant mass, with photosynthesis acting as the fundamental pathway for carbon assimilation. Subsequently, the discussion covers photosynthesis indices, their measurement techniques, and the challenges in establishing correlations between photosynthesis indices and yields. The review then delves into recent advancements, including leveraging natural variations, enhancing the electron transport chain, augmenting the efficiency of ribulose bisphosphate carboxylase/oxygenase (Rubisco), increasing CO2 concentration around Rubisco, initiatives like the C4 rice project, strategies for photorespiration bypass, and non-leaf photosynthesis contributions. The conclusion emphasizes future research directions such as advocating for the incorporation of photosynthesis within broader organismic processes, unraveling the complex link between photosynthesis and grain yield, developing efficient and direct methods for photosynthesis phenotyping, and assessing photosynthetic performance under actual field conditions.