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Ultrasonic pretreatment could improve the accessibility of enzymes to cellulosic fibers, hence expanding the access of enzymes to effectively remove ink from paper. In this study, enzymes were isolated from paddy rice husks and utilized for biodeinking process. The characterizations of deinking from the pulp filtrate and paper samples were carried out to understand the effect of bacterial cellulases and ultrasonic pretreatment with time variables. The results showed that cellulases from bacterial isolates had the ability to hydrolyze cellulose at 1.78 U/mL enzyme activity. Ink content measurement showed 2310 ppm of ink was removed in the paper samples with 15 min of ultrasound pretreatment and 2% cellulases. Meanwhile, morphological analysis showed significant differences in the surface areas between treated and untreated paper samples. The highest crystallinity index achieved was 70.9%, and thermal resistance was 21.0%. Other paper qualities such as stain decrease, and brightness level improvement were observed. The combination of 2% cellulases with optimum ultrasonication duration resulted in exceptional wastepaper recycling process. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Soil Sciences & Agricultural Engineering is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Plant Production is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This document is a guest editorial from the China Agricultural Economic Review. It discusses the implications of climate change on food systems and the need for policy research in addressing these challenges. The editorial highlights the importance of evidence-based solutions and the role of policymakers in both short-term emergency response and long-term resilience planning. The document also mentions specific research papers included in a special issue, which cover topics such as crop mix responses to climate conditions, the effects of natural disasters on agricultural trade, and the impact of green financing on agricultural efficiency. Overall, the editorial emphasizes the importance of informed and evidence-based policymaking in addressing climate change and transforming food systems. [Extracted from the article]

Fine-grained management of rice fields can enhance the yield and quality of rice crops. Challenges in achieving fine classification include interference from similar vegetation, the irregularity of natural field shapes, and complex scale variations. This paper introduces Rice Attention Cascade Network (RACNet), for the fine classification of rice fields in high-resolution satellite remote sensing imagery. The network employs the Hybrid Task Cascade network as the base framework and uses spectral and indices mixed multimodal data as input to reinforce the feature differentiation of similar vegetation. Initially, a Channel Attention Deformable-ResNet (CAD-ResNet) was designed to enhance the feature representation of rice on different channels. Deformable convolution improves the ability of CAD-ResNet to capture irregular field shapes. Then, to address the issue of complex scale changes, the multi-scale features extracted by the CAD-ResNet are progressively fused using an Asymptotic Feature Pyramid, reducing the loss of scale information between non-adjacent layers. Experiments on the Meishan rice dataset show that the proposed method is capable of accurate instance segmentation for fragmented or irregularly shaped rice fields. The evaluation metric AP50 of RACNet reaches 50.8%. [ABSTRACT FROM AUTHOR]

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the "knowledge features" learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the quality of 13 batches of rice and 17 batches of paddy. Differential scanning calorimetry curves, Fourier transform infrared spectroscopy, ultraviolet spectroscopy and electrochemical curves of 30 batches of rice and paddy were collected, and this paper utilized the quantitative fingerprints evaluated by using the systematic quantitative fingerprinting method, macro qualitative similarity (S m) and macro quantitative similarity (P m) to reflect the internal differences of the samples. A new quality assessment method was explored to quantify the obtained fingerprint profiles and to compare the correlation between the original fingerprints and the quantum fingerprints using a t-test. Using the parameters S m and P m as analysis criteria, hierarchical clustering analysis (HCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and the equal-weight integrated evaluation method were used to evaluate the quality of the four analysis methods. [ABSTRACT FROM AUTHOR]

Salinization is a process of increasing concentrations of salts in soil and water. Soil salinity is the most serious agricultural problem in irrigation agriculture in Tanzania. Severe salinity can cause significantly lower yields, food insecurity, and environmental degradation. If not properly addressed, soil salinization can accelerate and potentially expand to currently unaffected irrigated agricultural land. Despite the efforts made by the government and other stakeholders, rice production in irrigated rice fields in Tanzania is still experiencing low yields. Current rice yields are between 2.5 and 4 tons/ha, which is low compared to the average rice yields reported in other countries. Salinity problems are caused by the prevailing climate characterized by high evapotranspiration, geological and geographical characteristics of the area, quality of irrigation water, and inadequate land use practices. This paper aims to review available information on the causes and effects of soil salinity on irrigated rice fields in Tanzania and to provide information on management strategies to adjust soil salinity and improve soil fertility. References from books, papers, journals, online readings, and dissertation papers from different universities and institutions were used to get information for this paper review. In this review, the various reasons that cause soil salinity in irrigated fields in Tanzania have been identified, which are the nature of rocks, poor irrigation management, and non-compliance with established irrigation regulations. Also, the effects of soil salinity in Tanzania have resulted in a decrease in yields, an increase in costs of production, poor quality of products, and water shortages for irrigation. Lastly, some recommendations identified to solve the challenges of soil salinity include setting up a local drainage system on affected land, renovating some irrigation facilities, reclaiming the affected soil with gypsum and manure, and flushing all salt-affected water with non-salt water. [ABSTRACT FROM AUTHOR]

Rice is a staple food that feeds nearly half of the world's population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally. [ABSTRACT FROM AUTHOR]

Scientific and accurate estimation of rice yield is of great significance to food security protection and agricultural economic development. Due to the weak penetration of high frequency microwave band, most of the backscattering comes from the rice canopy, and the backscattering coefficient is highly correlated with panicle weight, which provides a basis for inversion of wet biomass of rice ear. To solve the problem of rice yield estimation at the field scale, based on the traditional water cloud model, a modified water-cloud model based on panicle layer and the radar data with Ku band was constructed to estimate rice yield at panicle stage. The wet weight of rice ear scattering model and grain number per rice ear scattering model were constructed at field scale for rice yield estimation. In this paper, the functional area of grain production in Xiashe Village, Xin'an Town, Deqing County, Zhejiang Province, China was taken as the study area. For the first time, the MiniSAR radar system carried by DJI M600 UAV was used in September 2019 to obtain the SAR data with Ku band under polarization HH of the study area as the data source. Then the rice yield was estimated by using the newly constructed modified water-cloud model based on panicle layer. The field investigation was carried out simultaneously for verification. The study results show: the accuracies of the inversion results of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel B were 95.03% and 94.15%; and the accuracies of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel C+D+E were over 91.8%. In addition, different growth stages had effects on yield estimation accuracy. For rice at fully mature, the yield estimation accuracies of wet weight of ear and grain number per ear were basically similar, both exceeding 94%. For rice at grouting stage, the yield estimation accuracy of wet weight of ear was 92.7%, better than that of grain number per ear. It was proved that it can effectively estimate rice yield using the modified water-cloud model based on panicle layer constructed in this paper at panicle stage at field scale. [ABSTRACT FROM AUTHOR]

Weeds are one of the greatest threats to the growth of rice, and the loss of crops is greater in the early stage of rice growth. Traditional large-area spraying cannot selectively spray weeds and can easily cause herbicide waste and environmental pollution. To realize the transformation from large-area spraying to precision spraying in rice fields, it is necessary to quickly and efficiently detect the distribution of weeds. Benefiting from the rapid development of vision technology and deep learning, this study applies a computer vision method based on deep-learning-driven rice field weed target detection. To address the need to identify small dense targets at the rice seedling stage in paddy fields, this study propose a method for weed target detection based on YOLOX, which is composed of a CSPDarknet backbone network, a feature pyramid network (FPN) enhanced feature extraction network and a YOLO Head detector. The CSPDarknet backbone network extracts feature layers with dimensions of 80 pixels ⊆ 80 pixels, 40 pixels ⊆ 40 pixels and 20 pixels ⊆ 20 pixels. The FPN fuses the features from these three scales, and YOLO Head realizes the regression of the object classification and prediction boxes. In performance comparisons of different models, including YOLOv3, YOLOv4-tiny, YOLOv5-s, SSD and several models of the YOLOX series, namely, YOLOX-s, YOLOX-m, YOLOX-nano, and YOLOX-tiny, the results show that the YOLOX-tiny model performs best. The mAP, F1, and recall values from the YOLOX-tiny model are 0.980, 0.95, and 0.983, respectively. Meanwhile, the intermediate variable memory generated during the model calculation of YOLOX-tiny is only 259.62 MB, making it suitable for deployment in intelligent agricultural devices. However, although the YOLOX-tiny model is the best on the dataset in this paper, this is not true in general. The experimental results suggest that the method proposed in this paper can improve the model performance for the small target detection of sheltered weeds and dense weeds at the rice seedling stage in paddy fields. A weed target detection model suitable for embedded computing platforms is obtained by comparing different single-stage target detection models, thereby laying a foundation for the realization of unmanned targeted herbicide spraying performed by agricultural robots. [ABSTRACT FROM AUTHOR]

This paper proposes a model of variable rate automatic microcontroller-based irrigation system. Solar power is used as only the source of power to control the overall system. The sensor is placed on paddy field and these sensors continuously sense the water level and give the message to the farmer informing the water level without visiting the paddy fields. Thus, low-cost solar power can be the answer for all our energy needs. Solar powered smart irrigation systems are the answer to the Indian farmer. This system consists of solar powered water pump along with an automatic water flow control using a moisture sensor. It is the convenient solution for the present energy crisis for the farmers. This paper reveals a model of automatic micro controller-based irrigation system. This paper proposes a model of automatic microcontrollerbased irrigation system. Here solar energy act as the sole supplier of energy throughout. Sensors are placed on the field and these sensors continuously sense the water level and give the message to the farmer informing the water level without visiting the paddy fields. This paper proposes a model of automatic microcontroller-based irrigation system. This system reduces the cost and increases productivity. This system conserves electricity by reducing the usage of grid power and conserves water by reducing water losses. [ABSTRACT FROM AUTHOR]

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest. [ABSTRACT FROM AUTHOR]

Insufficient knowledge of the composition and variation of isoprenoid and branched glycerol dialkyl glycerol tetraethers (GDGTs) in agricultural soils exists, despite of the potential effect of different management types (e.g. soil/water and redox conditions, cultivated plants) on GDGT distribution. Here, we determined the influence of different soil management types on the GDGT composition in paddy (flooded) and adjacent upland (non-flooded) soils, and if available also forest, bushland and marsh soils. To compare the local effects on GDGT distribution patterns, we collected comparable soil samples in various locations from tropical (Indonesia, Vietnam and Philippines) and subtropical (China and Italy) sites. We found that differences in the distribution of isoprenoid GDGTs (iGDGTs) as well as of branched GDGTs (brGDGTs) are predominantly controlled by management type and only secondarily by climatic exposition. In general upland soil had higher crenarchaeol contents than paddy soil, which on the contrary was more enriched in GDGT-0. The GDGT-0 / crenarchaeol ratio was 3-27 times higher in paddy soil and indicates the enhanced presence of methanogenic archaea, which were additionally linked to the number of rice cultivation cycles per year (higher number of cycles was coupled with an increase in the ratio). The TEX86 values were 1.3 times higher in upland, bushland and forest soils than in paddy soils. In all soils brGDGT predominated over iGDGTs, with the relative abundance of brGDGTs increasing from subtropical to tropical soils. Higher BIT values in paddy soils compared to upland soils together with higher BIT values in soil from subtropical climates indicate effects on the amounts of brGDGT through differences in management as well as climatic zones. In acidic soil CBT values correlated well with soil pH. In neutral to alkaline soils, however, no apparent correlation but an offset between paddy and upland managed soils was detected, which may suggest that soil moisture may exert an additional control on the CBT in these soils. Lower MBT' values and calculated temperatures (TMC) in paddy soils compared to upland soils may indicate a management (e.g. enhanced soil moisture through flooding practises) induced effect on mean annual soil temperature (MST). [ABSTRACT FROM AUTHOR]

The rice‐fish system has gained significant interest in recent years because of its effective usage of limited land and freshwater resources. To scientifically guide the improvement of rice field fishery production, the data in this study were selected from the latest China Fishery Statistical Yearbook, and therefore the development of paddy aquaculture was investigated. In order to more precisely predict the production of rice‐fish in China, this paper introduces an artificial neural network with the SSA‐BP model, which solves the drawbacks of the BP neural network such as easy to fall into local optimum and slow convergence speed when it is used for prediction. Firstly, the SSA‐BP model incorporates the aquaculture area (split by water area), the national freshwater fish seedling output, the national end‐of‐year ownership of inland fishing vessels, the number of freshwater fisheries practitioners as input variables, and the production of rice‐fish as an output variable; secondly, the SSA optimization algorithm was used to find the optimal initial thresholds and weights for the BP neural network, and finally the SSA‐BP prediction model was constructed. The results revealed that the overall expansion of the rice field fishery was swift in the last five years, and the output of cultivated fish in China's rice fields rose by nearly 20% yearly in the past five years. Compared with the BP neural network and GA‐BP models, the accuracy of the SSA‐BP prediction model was enhanced by 61.01% and 16.15%, respectively, which was more suited for predicting the production of rice‐fish. [ABSTRACT FROM AUTHOR]

The interconnection between the objectives of territorial development and those of the agro-ecological transition highlights the value of past knowledge in the sustainable management of resources and agro-ecological systems. However, the lack of data creates difficulties for retrospection in rural areas. This paper contributes to the search for such knowledge from the past by developing an interactive methodology capable of combining heterogeneous information sources with the activation of local collective memory. Its effectiveness is based on ensuring the interoperability of information and communication in an environment simultaneously shaped by geoinformatics and 3D geovisualisations. This virtual environment fostered participation and interactivity, supported by representations of the paleo-landscape (Ottoman period). Furthermore, synergies were achieved between information sources, which were integrated into local spatial systems. The application example involved identifying a rice field that existed between the 15th and 19th centuries in Thessaly, Greece. It is an interesting case because the research results indicated that the location and organisation of the crop, combined with the spatio-temporal coordination required, ensured the sustainable use of natural resources. The interplay between information and communication facilitated community participation and the activation of its collective memory as an information source that enriched the search itself and local intelligence. [ABSTRACT FROM AUTHOR]

In the rainy season, which often has uncertain rainfall, crawler combine harvesters have difficulty traversing wet and soft rice fields. A large amount of clay is often accumulated on the track surfaces, resulting in frequent slipping and sinking, which greatly affects the operational performance and harvesting efficiency of crawler combine harvesters. To address this issue, this paper proposes a high-traction track grouser based on the structure of an ostrich's foot sole. First, a traction force mathematical model is constructed to analyze the interaction between a track grouser and wet and soft rice fields, and parameter optimization is conducted. Then, the bionic information of a dung beetle's non-smooth body surface is extracted, and the surface of the track is designed with biomimetic convex hull patterns based on the geometric similarity principle and adhesive experiments. Finally, the analysis results indicate that the optimized track grouser significantly improved the traction of the track in wet and soft rice fields. For the track plate with a bionic desorption convex hull pattern, a convex hull diameter of 6 mm, convex hull spacing of 8.25 mm, and convex hull height of 3 mm led to good adhesion reduction and desorption effects in wet and soft soil. [ABSTRACT FROM AUTHOR]

Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement. [ABSTRACT FROM AUTHOR]

Paddy-rice cultivation using the traditional continuous flooding method requires much water, up to 2500 L, to produce 1 kg of rice. Decreasing water availability is being exacerbated by climate dynamics, i.e., droughts and rainfall variability negatively affecting food security in developing regions, particularly Africa. Alternate wetting and drying (AWD) practice is a climate-smart water management strategy that, together with puddling (a critical field preparation process), significantly affects soil hydrological and physicochemical regimes, such as soil water dynamics and oxidation states in paddy fields. However, there are limited reviews on the effects and interaction of the AWD duration on hydrological conditions in the paddy-rice rhizosphere continuum under AWD practice at different rice growth stages. Our review synthesizes key scientific literature to examine water management and hydrological properties of paddy soils under AWD practice with climate change and sheds light on why farmers are skeptical in adopting the practice. To develop this paper, we reviewed scientific information from published journal articles, reliable reports, and our knowledge on paddy-rice cultivation and water management with climate change in Asia and Sub-Saharan Africa. Several studies confirm that AWD practice increases water–rice–crop productivity, yields, and reduces methane emissions. Limitations and challenges of AWD irrigation, including changes in soil structure that influence irrigation water application, variations in hydraulic conductivity caused by the duration and frequency of irrigation cycles, and frequent manual water level (WL) monitoring, are discussed. Opportunities to improve the integration of AWD strategies within government policies, irrigation schemes, and farmer acceptance due to skepticism, limited knowledge, and fear of unreliable water hindering adoption are highlighted. Future research suggestions include the following: (i) long-term measurement of water stress indices using infrared thermometers; (ii) seasonal suitability mapping using NDVI, GIS, and remote sensing; and (iii) application of smart sensors based on the Internet of Things (IoT) to address AWD challenges for precision water management in paddy fields with climate change. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is a staple food of more than half of the population of the world and provides food security and livelihoods for millions of population. Direct seeding of rice (DSR) refers to the process of direct sowing the seeds in the field rather than by transplanting seedlings from the nursery. Direct seeded rice is an age old practice and before the introduction of green revolution in India. Rainfed rice was often broadcasted into moist soil and crops were highly prone to weed competition resulted into low yield. Rice ecosystems and establishment methods determined the weed spectrum and degree of infestation in rice field (DSR). Weeds are considered as a major biological constraint in direct seeded rice. In direct seeded rice, weed management depend on weed flora, critical period of weed control, availability of water and method to be adopted. Integrated weed management (IWM) is essential to achieve the long term, sustainable and economic management of weeds in direct seeded rice. The literature regarding the direct seeded rice, critical period of crop weed competition, different types of weed flora and different methods for the effective and sustainable management of weeds in direct seeded rice are reviewed in this paper. [ABSTRACT FROM AUTHOR]

In this study, we investigate the potential effects of the Regional Comprehensive Economic Partnership (RCEP) agreement on Vietnam's rice exports from 2000 to 2016. For this analysis, we employ the panel data augmented gravity model with some modifications. The estimated results suggest a positive relationship between Vietnam's per capita export and the capita real gross domestic product (GDP) of the importer, Vietnam's rice production, export price, and ASEAN dummy. We confirm that there is a negative relationship between Vietnam's per capita rice export value and import country agricultural land and import tariffs. Additionally, this paper forecasts Vietnam's rice export value per capita at each market in RCEP partners by using the autoregressive model. Our forecast results show that among RCEP members, Vietnam has potential markets if import tariff reductions are lowered in Singapore, Brunei, Korea, Japan, and Malaysia. To reap the benefits of rice exports' potential, Vietnam must move from low-grade and cheap rice to high-quality rice production instead of expanding rice production and exports with the following implications: changing its present rice trading partners to potential markets and adopting market-oriented policies to meet the international demand and to increase the export elasticity of rice. [ABSTRACT FROM AUTHOR]

Paddy (Oryza sativa) is one of the most consumed food grains in the world. The process from its sowing to consumption via harvesting, processing, storage and management require much effort and expertise. The grain quality of the product is heavily affected by the weather conditions, irrigation frequency, and many other factors. However, quality control is of immense importance, and thus, the evaluation of grain quality is necessary. Since it is necessary and arduous, we try to overcome the limitations and shortcomings of grain quality evaluation using image processing and machine learning (ML) techniques. Most existing methods are designed for rice grain quality assessment, noting that the key characteristics of paddy and rice are different. In addition, they have complex and expensive setups and utilize black-box ML models. To handle these issues, in this paper, we propose a reliable ML-based IoT paddy grain quality assessment system utilizing affordable sensors. It involves a specific data collection procedure followed by image processing with an ML-based model to predict the quality. Different explainable features are used for classifying the grain quality of paddy grain, like the shape, size, moisture, and maturity of the grain. The precision of the system was tested in real-world scenarios. To our knowledge, it is the first automated system to precisely provide an overall quality metric. The main feature of our system is its explainability in terms of utilized features and fuzzy rules, which increases the confidence and trustworthiness of the public toward its use. The grain variety used for experiments majorly belonged to the Indian Subcontinent, but it covered a significant variation in the shape and size of the grain. [ABSTRACT FROM AUTHOR]

An investigation into oomycete diversity in rice paddies of Fars Province in Iran led to the identification of two new Pythium sensu lato (s.l.) species as Globisporangium izadpanahii sp. nov. and Pythium banihashemianum sp. nov. The identification was based on morphological and physiological features as well as on the phylogenetic analysis of nuclear (ITS and βtub) and mitochondrial (cox1 and cox2) loci using Bayesian inference and Maximum Likelihood. The present paper formally describes these two new species and defines their phylogenetic relationships with other congeneric species. According to multiple gene genealogy analysis, G. izadpanahii sp. nov. was grouped with other species of Globisporangium (formerly, clade G of Pythium s.l.) and was closely related to both G. nagaii and the recently described G. coniferarum. The second species, designated P. banihashemianum sp. nov., was grouped with other species of Pythium sensu stricto (formerly, clade B of Pythium s.l.) and, according to the phylogenetic analysis, shared an ancestor with P. plurisporium. The production of globose hyphal swellings was a major characteristic of G. izadpanahii sp. nov., which did not produce vesicles and zoospores. In pathogenicity tests on rice seedlings, P. banihashemianum sp. nov. isolates were highly pathogenic and caused severe root and crown rot, while G. izadpanahii sp. nov. isolates were not pathogenic. [ABSTRACT FROM AUTHOR]

Rice cropped policy has been constantly changing, affecting the value of irrigated land. This study aims to examine distribution patterns of irrigated land value from a new perspective of uncertainties in single- and double-cropped rice policies in China. The real options approach was employed to calculate irrigated land value under policy uncertainty, and Moran's index was applied to identify the spatial distribution characteristics of irrigated land value at the provincial level. The empirical results were as follows: significantly positive spatial correlations existed between the land value under two cropped rice policies. The northeast region of China had a low-value clustering effect based on geographical characteristics. In contrast, the southern provinces of China had a high-value clustering effect based on the criteria of administrative regions. This study considered policy uncertainty when evaluating the economic effect on irrigated land when revealing spatial correlation in land value. We provide a theoretical and empirical basis for the formulation of cropped rice policies. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper provides a methodology for analyzing the trajectory of the Crested Ibis (Nipponia nippon): its clustering and mining habitats, trajectory-data supplementation, the analysis of behavioral patterns, and finally, potential habitat mining. It was found that the characteristics of the Crested Ibis habitat in nature reserves include proximity to water sources and cultivated land close to woodlands. Additionally, there is a certain aggregation and sharing of night roosting sites, which are easily disturbed by human activities, and there is a strong correlation between the spatial proximity of the Crested Ibis night. Although human behavior threatens the survival of the Crested Ibis, they still depend on human behavior, mainly for food and water. Moreover, farmers are encouraged to store water in paddy fields in winter and to avoid using harmful substances such as pesticides. Luoshan County provides a protected area for the Crested Ibis; it was analyzed and found to be appropriately located, with an excellent overall habitat-rating. The Chinese Government has also set up corresponding ecological and environmental protection zones, and it is essential to provide solutions for the reintroduction of the Crested Ibis. The Crested Ibis (Nipponia nippon) is an endangered animal with an extremely high ecological, humanistic, and scientific value. However, this species still faces survival challenges, due to rapidly shrinking foraging grounds, the serious interference of human behavior, and increased habitat requirements. Geographical environment is a significant factor affecting Crested Ibis behavior-pattern analysis and habitat protection. The spatial and temporal trajectory contains habitat location and period information, a vital record of the Crested Ibis' habits, and the basis of all research. Nevertheless, there are only a handful of studies on the missing trajectory data and fusing multiple sources of environmental data-research methods. We studied the spatial and temporal habitat use of the tracked Crested lbis by fusing multiple data-sources in China. This paper adopts the LSTM (long short-term memory) model to supplement the missing trajectory data and perform cluster mining; and a random forest model is used to predict the habitat of the Crested Ibis with high fitting accuracy (R2 = 84.9%). The results show that the Crested Ibis distribution-pattern is characterized by high altitude and proximity to woodland and rivers. Additionally, the habitat dependence on the village implicates human agricultural activities in positively impacting its reproduction. This paper provides a complete method for analyzing Crested Ibis' spatial and temporal trajectory by fusing multi-source data, which is crucial for protecting the survival and reproduction of the Crested Ibis. [ABSTRACT FROM AUTHOR]

Composite agricultural systems with permanent maize cultivation in the uplands and irrigated rice in the valleys are very common in mountainous Southeast Asia. The soil loss and fertility decline of the upland fields is well documented, but little is known about reallocation of these sediments within the landscape. In this study, a turbidity-based linear mixed model was used to quantify sediment inputs, from surface reservoir irrigation water and from direct overland flow, into a paddy area of 13 hectares. Simultaneously, the sediment load exported from the rice fields was determined. Mid-infrared spectroscopy was applied to analyze sediment particle size. Our results showed that per year, 64 Mg ha-1 of sediments were imported into paddy fields, of which around 75 % were delivered by irrigation water and the remainder by direct overland flow during rainfall events. Overland flow contributed one third of the received sandy fraction, while irrigated sediments were predominantly silty. Overall, rice fields were a net sink for sediments, trapping 28 Mg ha-1 a-1 or almost half of total sediment inputs. As paddy outflow consisted almost exclusively of silt- and clay-sized material, 24 Mg ha-1 a-1 of the trapped amount of sediment was estimated to be sandy. Under continued intensive upland maize cultivation, such a sustained input of coarse material could jeopardize paddy soil fertility, puddling capacity and ultimately also food security of the inhabitants of these mountainous areas. Preventing direct overland flow from entering the paddy fields, however, could reduce sand inputs by up to 34 %. [ABSTRACT FROM AUTHOR]

Our understanding of how net global warming potential (NGWP) and greenhouse gas intensity (GHGI) is affected by management practices aimed at food security with respect to rice agriculture remains limited. In the present study, a 5 year field experiment was conducted in China to evaluate the effects of integrated soil-crop system management (ISSM) on NGWP and GHGI after accounting for carbon dioxide (CO2) emissions from all sources (methane, CH4, and nitrous oxide, N2O, emissions, agrochemical inputs, Ei, and farm operations, Eo) and sinks (i.e., soil organic carbon, SOC, sequestration). For the improvement of rice yield and agronomic nitrogen use efficiency (NUE), four ISSM scenarios consisting of different nitrogen (N) fertilization rates relative to the local farmers' practice (FP) rate were carried out, namely, N1 (25% reduction), N2 (10% reduction), N3 (FP rate) and N4 (25% increase). The results showed that compared with the FP, the four ISSM scenarios, i.e., N1, N2, N3 and N4, significantly increased the rice yields by 10, 16, 28 and 41% and the agronomic NUE by 75, 67, 86 and 82%, respectively. In addition, compared with the FP, the N1 and N2 scenarios significantly reduced the GHGI by 14 and 18%, respectively, despite similar NGWPs. The N3 and N4 scenarios remarkably increased the NGWP and GHGI by an average of 67 and 36%, respectively. In conclusion, the ISSM strategies are promising for both food security and environmental protection, and the ISSM scenario of N2 is the optimal strategy to realize high yields and high NUE together with low environmental impacts for this agricultural rice field. [ABSTRACT FROM AUTHOR]

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Measurements of hyperspectral canopy reflectance provide a detailed snapshot of information regarding canopy biochemistry, structure and physiology. In this study, we collected five years of repeated canopy hyperspectral reflectance measurements for a total of over 100 site visits within the flux footprints of two eddy covariance towers at a pasture and rice paddy in Northern California. The vegetation at both sites exhibited dynamic phenology, with significant inter-annual variability in the timing of seasonal patterns that propagated into inter-annual variability in measured hyperspectral reflectance. We used partial least-squares regression (PLSR) modeling to leverage the information contained within the entire continuous canopy reflectance spectra (400-900 nm) in order to investigate questions regarding the connection between measured hyperspectral reflectance and landscape-scale fluxes of net ecosystem exchange (NEE) and gross primary productivity (GPP) across multiple timescales, from instantaneous flux to monthly-integrated flux. With the PLSR models developed from this large dataset we achieved a high level of predictability for both NEE and GPP flux in these two ecosystems, where the R² of prediction with an independent validation dataset ranged from 0.24 to 0.69. The PLSR models achieved the highest skill at predicting the integrated GPP flux for the week prior to the hyperspectral canopy reflectance collection, whereas the NEE flux often achieved the same high predictive power at the daily- through monthly-integrated flux timescales. The high level of predictability achieved by PLSR regression in this study demonstrated the potential for using repeated hyperspectral canopy reflectance measurements to help partition NEE measurements into its component fluxes, GPP and ecosystem respiration, and for using continuous hyperspectral reflectance measurements to model regional carbon flux in future analyses. [ABSTRACT FROM AUTHOR]

Rice serves as a staple food across various continents worldwide. The rice plant faces significant threats from a range of fungal, bacterial, and viral pathogens. Among these, rice false smut disease (RFS) caused by Villosiclava virens is one of the devastating diseases in rice fields. This disease is widespread in major ricegrowing regions such as China, Pakistan, Bangladesh, India, and others, leading to significant losses in rice plantations. Various toxins are produced during the infection of this disease in rice plants, impacting the fertilization process as well. This review paper lightens the disease cycle, plant immunity, and infection process during RFS. Mycotoxin production in RFS affects rice plants in multiple ways, although the exact phenomena are still unknown. [ABSTRACT FROM AUTHOR]

Rice production is the agricultural activity with the highest energy consumption and carbon emission intensity. Water and fertilizer management constitutes an important part of energy input for rice production and a key factor affecting greenhouse gas emissions from paddy fields. Water–fertilizer integration management (AIM) is an automated water and fertilizer management system for large-scale rice production, which can effectively save water and fertilizer resources. At present, the energy utilization and environmental impact of AIM in rice production are not clear. To clarify whether AIM is a water and fertilizer management measure that combines energy conservation and carbon emission reduction, a comparative study between the widely used farmers' enhanced water and fertilizer management (FEM) in China and AIM was conducted in this paper. Field experiments were conducted to evaluate the rice yield, carbon emission, energy utilization, and economic benefits of the two management methods. The results showed that AIM reduced water and fertilizer inputs, energy inputs, and economic costs by 12.18–28.57%, compared to FEM. The energy utilization efficiency, energy profitability, and energy productivity under AIM were improved by 11.30–12.61%. CH4 and N2O emissions and carbon footprint were reduced by 20.79%, 6.51%, and 16.39%, respectively. Compared with FEM, AIM can effectively improve the utilization efficiency of water and fertilizer resources and reduce carbon emissions. This study presents a mechanized water and fertilizer management approach suitable for large-scale rice production systems in China. By analyzing rice yield, resource utilization efficiency, and environmental benefits, AIM can serve as a crucial management strategy for enhancing productivity, economic returns, and environmental conservation within profitable rice production systems. In the future, further investigation into the impact of AIM on the microbial mechanisms underlying rice yield formation and greenhouse gas emissions is warranted. [ABSTRACT FROM AUTHOR]

Straw is an important source of organic fertilizer for soil enrichment, however, the effects of different nitrogen(N) application rates and depths on straw decomposition microorganisms and carbon and nitrogen cycling under full straw return conditions in cool regions of Northeast China are not clear at this stage. In this paper, we applied macro-genome sequencing technology to investigate the effects of different N application rates (110 kg hm−2, 120 kg hm−2, 130 kg hm−2, 140 kg hm−2, 150 kg hm−2) and depths (0–15 cm, 15–30 cm) on straw decomposing microorganisms and N cycling in paddy fields in the cool zone of Northeast China. The results showed that (1) about 150 functional genes are involved in the carbon cycle process of degradation during the degradation of returned straw, of which the largest number of functional genes are involved in the methane production pathway, about 42, the highest abundance of functional genes involved in the citric acid cycle pathway. There are 22 kinds of functional genes involved in the nitrogen cycle degradation process, among which there are more kinds involved in nitrogen fixation, with 4 kinds. (2) High nitrogen application (150 kg hm−2) inhibited the carbon and nitrogen conversion processes, and the abundance of straw-degrading microorganisms and nitrogen-cycling functional genes was relatively high at a nitrogen application rate of 130 kg hm−2. (3) Depth-dependent heterogeneity of the microbial community was reduced throughout the vertical space. At 71 days of straw return, the nitrogen cycling function decreased and some carbon functional genes showed an increasing trend with the increase of straw return depth. The nitrogen cycle function decreased with the increase of straw returning depth. The microbial community structure was best and the abundance of functional genes involved in the nitrogen cycling process was higher under the conditions of 0–15 cm of returning depth and 130 kg hm−2 of nitrogen application. [ABSTRACT FROM AUTHOR]

Monitoring crop phenology is essential for managing field disasters, protecting the environment, and making decisions about agricultural productivity. Because of its high timeliness, high resolution, great penetration, and sensitivity to specific structural elements, synthetic aperture radar (SAR) is a valuable technique for crop phenology estimation. Particle filtering (PF) belongs to the family of dynamical approach and has the ability to predict crop phenology with SAR data in real time. The observation equation is a key factor affecting the accuracy of particle filtering estimation and depends on fitting. Compared to the common polynomial fitting (POLY), machine learning methods can automatically learn features and handle complex data structures, offering greater flexibility and generalization capabilities. Therefore, incorporating two ensemble learning algorithms consisting of support vector machine regression (SVR), random forest regression (RFR), respectively, we proposed two machine learning-aided particle filtering approaches (PF-SVR, PF-RFR) to estimate crop phenology. One year of time-series Sentinel-1 GRD SAR data in 2017 covering rice fields in Sevilla region in Spain was used for establishing the observation and prediction equations, and the other year of data in 2018 was used for validating the prediction accuracy of PF methods. Four polarization features (VV, VH, VH/VV and Radar Vegetation Index (RVI)) were exploited as the observations in modeling. Experimental results reveals that the machine learning-aided methods are superior than the PF-POLY method. The PF-SVR exhibited better performance than the PF-RFR and PF-POLY methods. The optimal outcome from PF-SVR yielded a root-mean-square error (RMSE) of 7.79, compared to 7.94 for PF-RFR and 9.1 for PF-POLY. Moreover, the results suggest that the RVI is generally more sensitive than other features to crop phenology and the performance of polarization features presented consistent among all methods, i.e., RVI＞VV＞VH＞VH/VV. Our findings offer valuable references for real-time crop phenology monitoring with SAR data. [ABSTRACT FROM AUTHOR]

Background: Mastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition. Methods: In the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification. Results: The solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m2. Conclusions: The study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m2 to 2000 m2. [ABSTRACT FROM AUTHOR]

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines. [ABSTRACT FROM AUTHOR]

To improve wheel trafficability in soft and muddy soils such as paddy fields, a bionic walking wheel is designed based on the structural morphology and movement mode of the feet of waders living in marshes and mudflats, similar to the muddy soil of paddy fields. The bionic walking wheel adopts the arrangement of double-row wheel legs and staggered arrays to imitate the walking posture of waders. The two legs move alternately, cooperate with each other, and improve the smoothness of movement. The cam inside the bionic walking wheel is used to control the movement mode of the feet. The flippers open before touching the ground to increase the contact area and reduce sinking, and the toes bend and grip the ground while touching the ground to increase traction. Multi-rigid-body dynamics software (Adams View 2020) is used to simulate the movement of the wheel during the wading process, and the movement coordination and interference between the wheel legs are analyzed. The simulation results show that there is no interference between the parts and that the movement smoothness is good. The interaction between the bionic walking wheel and muddy soil was analyzed via coupled EDEM–ADAMS simulation, and the simulation analysis and experiments were conducted and compared with those for a common paddy wheel. The results showed that the bionic walking wheel designed in this paper improved the drawbar pull by 113.56% compared with that of a common paddy wheel and had better anti-sinking performance. By analyzing the effect of toe grip on traction, it was found that the soil under the feet can be disturbed to provide greater traction when the toe is bent downward. This study provides a reference for improving the trafficability of walking mechanisms in soft and muddy soils, such as paddy fields. [ABSTRACT FROM AUTHOR]

Greenhouse gas emissions (GHG) and nutrient loss are considered to be critical environmental issues facing rice field ecosystems. Biochars (BCs), as carbon-rich materials with porous structures, large specific surface areas, and enriched surface functional groups, have shown great potential for improving environmental problems in rice fields and increasing rice yields. However, thus far, we still lack an adequate summary and review of the performance characteristics of BCs and their environmental behavior in paddy soils. In this paper, we critically review the positive effects of BC application on the growth and yield of rice, nutrient loss reduction, and GHG reduction. Biomass type, pyrolysis temperature, and modification method are considered to be the key factors that determine the performance characteristics of BCs. The application of BCs could promote rice yield and mitigate CO2, N2O, and CH4 emissions by improving soil physicochemical properties and microbial communities, providing nutrient sources, and reducing nutrient losses. Finally, this paper illustrates the potential ecological risks of BC application on paddy fields, including the risks of inconclusive research results and secondary pollution. These shortcomings need to be addressed in future research to ensure the sustainability of BC application. [ABSTRACT FROM AUTHOR]

Purpose: The paper investigates the effects of climate change along with ecological and carbon footprint on rice crop production in India during 1982–2016. Design/methodology/approach: The autoregressive distributed lag (ARDL), canonical cointegration regression (CCR) and fully modified ordinary least square (FMOLS) models are used in the paper. Findings: A long-run relationship is found between climate change and rice production in India. Results report that ecological footprint and carbon footprint spur long-term rice production. While rainfall boosts rice crop productivity in the short term, it has a negative long-term impact. Further, the findings of ARDL models are validated by other cointegration models, i.e., the FMOLS and CCR models. Research limitations/implications: This study provides insights into the role of ecological footprint and carbon footprint along with climate variables in relation to rice production. Originality/value: In the literature, the effects of ecological and carbon footprint on rice production are missing. Therefore, this is the first study to empirically examine the impact of climate change along with ecological footprint and carbon footprint on rice production in India. [ABSTRACT FROM AUTHOR]

The phylogeny of field crabs has been under profuse discussion for several decades. At this juncture, this paper reassesses the "decades old" phylogenetic status of an edible field crab, hitherto referred to as Paratelphusa hydrodromous , inhabiting the paddy fields of Chelari village (Kerala, India). After Alcock's (1910) description, there has hardly been any serious attempt at ascertaining its precise taxonomic status. Through an integrated approach, using parsimony analysis and molecular methods, the present study reveals the candidate form's (species') closer affinity to the genus Oziotelphusa. Comparison of morphological and molecular characteristics with its closest phylogenetic neighbours (O. biloba , O. wagrakarowensis , O. kerala and O. aurantia) has shown that the candidate form is a taxonomically distinct entity, qualified to be considered as an unreported species; its suggested revised nomenclature is Oziotelphusa chelarensis , reminiscent of the village Chelari. Further, the paper views the relevance of this taxonomic study from conservational angles as well. [ABSTRACT FROM AUTHOR]

This paper aimed to obtain appropriate indigenous arbuscular mycorrhizal fungi types and determine the right dosage of phosphate fertilizer to increase rice yields under the System of Rice Intensification Cultivation. The research design used a factorial randomised block design with three replications. Factor I was a type of arbuscular mycorrhizal fungi (Glomus sp. 2, Glomus sp. 3, and Sclerocystis sp) on Agam river sand media with the number of spores being 120 spores per 100 grams of sand media. The dose of arbuscular mycorrhizal fungi was1.6 tons/ha or 15 g/plant. Factor II was the dose of phosphate fertilizer (P = 0%, P1 = 25%, P2 = 50%, and P3 = 75%) of the recommended dose which was applied for each treatment at these rates:: P = 0 g/plot, P1 = 20 g/plot, P2 = 40 g/plot, and P3 = 60 g/plot. The data was analsed for variance using the SAS program. The results showed that Glomus sp. 3 with 50% phosphate fertilizer efficiency had statistically significant different results (p <0.05) for plant height, number of tillers, number of panicles, number of filled grains, weight of 1000 seeds, and yield per hectare compared to the other treatments. The nutrient content of paddy fields pH, CN-ratio, total N, P available and CEC showed a statistically significant difference between the arbuscular mycorrhizal fungi types and the P fertilizer efficiency treatment. No statistically significant difference was found in the CN ratio between Glomus sp. 3 and Sclerocystis sp., but there was a statistically significant difference from Glomus sp. 2. [ABSTRACT FROM AUTHOR]

This paper has taken a 25-horsepower tractor as the object and developed a distributed drive electric tractor (DDET), which may be applied to farms in paddy and upland fields. First, we designed the structure of the chassis drive system in the DDET, then analyzed the dynamic performances, built an evaluation index of dynamic performance for the DDET, and researched the influencing factors of dynamic performances. Moreover, we built the simulation model and studied the dynamic performance based on Advisor software in different working conditions. Finally, we developed the testing tractor of the DDET for test verification. Results show that the traction of DDET is about 5400 N, and the time from being static to being accelerated to a maximum speed of 28.15 km/h is about 5.5279 s. The theoretical analysis results, simulation results, and testing tractor verification results of the DDET designed in this paper are basically consistent, and the overall dynamic performance of the DDET is good. [ABSTRACT FROM AUTHOR]

Is an agricultural corporation more efficient than a traditional family farm? This paper attempts to answer this question by examining the technical and allocative efficiency of family farms and agricultural corporations. To do so, it applies the stochastic production frontier method in panel data built on the family farms and agricultural corporations in the Japanese rice sector and focuses on comparing the technical and allocative efficiency of the two production forms at the same scale of operation. Results reveal that family farms have a significant advantage over agricultural corporations in technical efficiency at each level of scale of operation. In both production forms, as the scale of operation increases, the technical efficiency correspondingly rises. However, the disparity in technical efficiency diminishes between the two production forms as their land size increases. In contrast, the allocative efficiency of different factors differs between family farms and agricultural corporations at different scales of land size. Overall, family farms show superiority in the allocative efficiency of labour, and agricultural corporations exhibit superiority in the allocative efficiency of agricultural capital. Last, decomposition of total productivity progress (TFP) reveals that family farms have positive TFP change which is mainly attributable to a positive and large allocative component, while agricultural corporations undergo negative TFP change due to its negative and large allocative component. Moreover, the results intimate that technical progress and technical efficiency improvement are faster in agricultural corporations than in family farms. [ABSTRACT FROM AUTHOR]

A suitable irrigation pattern is of great significance for reducing greenhouse gas emissions. In this study, field experiments and a denitrification-decomposition (DNDC) model were used to study the global warming potential based on CH4, N2O and CO2 fluxes under flooding irrigation and controlled irrigation in paddy fields in the Erhai Lake basin. The results showed that the average value of CH4 flux under controlled irrigation was lower than that under flooding irrigation, with a reduction range of 43.21% to 48.88%, however, the average value of the N2O and CO2 fluxes from paddy field under controlled irrigation were higher than those under flooding irrigation. Controlled irrigation patterns can significantly reduce the global warming potential in paddy fields based on CH4, N2O and CO2 fluxes. Controlled irrigation can effectively reduce the global warming potential per unit yield. For water management in the Erhai Lake basin, it is recommended the controlled irrigation treatment of soil moisture with an upper limit of 100% and a lower limit of 75-85% with irrigation, and a maximum surface water depth of 150-200 mm lasting for five days after precipitation from the jointing-booting stage to the milk stage. [ABSTRACT FROM AUTHOR]

The identification and classification of pests in rice field are the prerequisites of early warning systems for pest dis-asters. Among these pests, the rice planthoppers cause the most serious damage. However, the existing deep learning models for rice planthopper recognition are characterized by large size and numerous parameters, which makes them unsuitable for the deployment on embedded devices with limited computational resources. To address this issue, a lightweight rice planthopper recognition model based on YOLOv5 is proposed in this paper. In the model, a lightweight convolutional network named GhostNet is employed as the backbone to reduce the operational parameters. Additionally, a convolutional attention module (CBAM) is integrated into the backbone network to effectively enhance the transmission of deep information, so as to improve the model's ability of recognizing rice planthopper images. The original CIoU loss function is replaced by the SIoU loss function to expedite model convergence. Experimental results demonstrate that the modified model achieves the mAP@0.5 as 82.8%, with the parameter count of 3.12x106 and the model size of 7.2MB. Compared to the original model, it is a reduction of 46.7% in size and 43.3% in parameters, with a minor accuracy loss of 0.1%. Clearly, the improved model can achieve lightweight characteristics and robust performance, and thus it provides a theoretical and practical foundation for early warning systems against rice planthopper infestations in rice fields. [ABSTRACT FROM AUTHOR]