Rice Production Areas Research Articles

Study on Properties and Micro-Mechanism of RHB-SBS Composite-Modified Asphalt.

Rice husk biochar (RHB) is a renewable agricultural waste, and its fixation on pavements helps develop environmentally friendly, economical, and sustainable asphalt pavements. This paper used RHB to replace part of styrene-butadiene-styrene (SBS) for the composite modification study of matrix asphalt. The high- and low-temperature properties and microscopic mechanisms of the composite-modified asphalt were studied through a series of tests. The results showed that, compared with SBS-modified asphalt, the softening point, viscosity, complex shear modulus, stiffness modulus, and rutting factors of RHB-SBS composite-modified asphalt were improved. In contrast, the ductility and creep rate were slightly decreased, indicating an improvement in the high-temperature performance of composite-modified asphalt, but a slight decrease in its low-temperature performance. The process of RHB and SBS composite modification was mainly physical blending, with only a small number of chemical reactions, and no new functional groups were generated. The porous structure of RHB enables it to adhere better to the network crosslinked continuous phase system formed by SBS and matrix asphalt. This results in composite-modified asphalt with good high-temperature storage stability and rheological properties. Therefore, RHB-SBS composite-modified asphalt can be applied to high-temperature areas and rice-producing areas, and the optimal content of RHB is suggested to be 15%.

Future climate change for major agricultural zones in China as projected by CORDEX-EA-II, CMIP5 and CMIP6 ensembles

Understanding future climate change is crucial for securing and managing water supplies necessary for agricultural activities in China, particularly in rainfed agriculture regions. This paper evaluates the projected climate change for nine major agricultural zones in China, using ensembles of regional climate models (RCMs) simulated under the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II) framework. The simulations were conducted for two climate scenarios (RCP2.6 and RCP8.5) at a 25 km grid spacing, with three RCMs (RegCM4, REMO, and WRF) driven by multiple CMIP5 global climate models (GCMs). The study compares the RCM simulations to the driving CMIP5 and the recent CMIP6 ensembles, focusing on surface temperature, precipitation, and surface relative humidity for the present day (1981–2010), mid-future (2036–2065), and far future (2070–2099) periods. The model evaluations indicate that the GCM and RCM simulations are consistent with observations, with RCMs showing better performance than the driving GCMs. The results show that both GCMs and RCMs project increased temperature and precipitation in most agricultural zones of China in the future, with the CMIP6 ensembles projecting the largest temperature increase and the CORDEX-EA-II ensemble showing the finest spatial details. Relative humidity is projected to decrease over major rice-producing areas of South China in the CORDEX-EA-II, CMIP5, and CMIP6 ensembles. This study provides valuable information to support climate change adaptation and mitigation efforts in China, particularly in the agricultural sectors.

Morphology and biological characteristics of some popular weedy rices in the Mekong delta of Vietnam

The growing trend of cropping intensification, mechanisation, the use of poor-quality rice seeds, and the shift in rice cultivation methods from transplanting to direct sowing to meet food security and export demand are causes of the emergence and spread of weedy rice (Oryza sativa f. spontanea) in the Mekong delta in recent years. Therefore, it is necessary to study the agronomic, morphological, and physiological characteristics of collected weedy rice lines to help identify, analyse, and distinguish weedy rice in the rice field. Out of a total of 28 weedy rice lines planted, selected, and observed from 248 weedy rice samples collected in the main rice production areas in the Mekong delta, there are 12 weedy rice lines with one or more morphological and biological characteristics that can distinguish among them and with cultivated rice varieties, such as growing duration, plant height, leaf colour and shape, stem colour and shape, awn or without awn, husk colour, seed length, grain colour. These key characteristics can be used to distinguish and identify weedy rice lines from rice varieties grown at different growth stages in the field. However, some weedy rice lines are sharing major morphological features with the cultivated rice variety such as short or similar growing duration, shorter or similar height, similar husk colour, seed shape. Those weedy rice lines are not easy to identify and eliminate on the rice field, leading to the possibility of becoming widespread and difficult to control in rice production in the Mekong delta.

Effects of Partial Replacement of Nitrogen Fertilizer with Organic Fertilizer on Rice Growth, Nitrogen Utilization Efficiency and Soil Properties in the Yangtze River Basin.

Cake fertilizer and dairy manure were used as experimental materials to carry out a 9-year (2012-2020) field experiment in the main rice production areas in the Yangtze River basin. Different fertilization modes were used (no fertilization, CK; chemical fertilizer application alone, HY; reduced fertilization with chemical fertilizer application, RF; cake fertilizer replacement of nitrogen fertilizer, CFR; and dairy manure replacement of nitrogen fertilizer, DMR). Changes in the total rice yield, yield components, absorption of nitrogen, soil pH, organic matter, total nitrogen, and soil bulk density under different fertilization treatments were analyzed. The results show that organic fertilizer replacement leads to a stable and high rice yield. The 9-year average rice yields of the CFR and DMR treatments were 60.0% and 61.5% higher than that of CK. The nitrogen uptake of the CFR and DMR treatments was also higher than that of the other treatments. The nitrogen recovery efficiency in the current season could be increased by 16.37-22.89%, and after 9 years of testing, the soil total nitrogen contents of CFR and DMR increased by 0.23-0.85 g·kg-1 compared to the other treatments. The available P and K contents of DMR increased by 30.17 mg·kg-1 and 22.02 mg·kg-1 compared with HY, respectively. The soil bulk density was reduced by 0.08 g·cm-3. Generally, the effects of dairy manure replacement were better than those of cake fertilizer. This is an important method that can be used to fertilize the soil and foster sustainable soil utilization in the rice-growing area of the Yangtze River Basin, as a long-term partial replacement for chemical nitrogen fertilizer.

Impact of Agricultural Division of Labor on Fertilizer Reduction Application: Evidence from Western China.

Nowadays, environmental friendly agriculture has become the world trend of modern agricultural development; fertilizer reduction application is an essential way to achieve sustainable development goals. With the deepening development of the agricultural specialized division of labor and socialized services, the division of labor economy can promote the economic input of fertilizer. Based on 540 survey data of farmers in the main rice-producing areas of Sichuan Province, this paper constructs a theoretical analytical framework for the impact of agricultural division of labor on fertilizer reduction application. A binary probit model was used to empirically analyze the effect of agricultural division of labor on fertilizer reduction application, and its mechanism was examined. The results show that: (1) both horizontal and vertical agricultural divisions of labor have positive and significant effects on the reduction in fertilizer application by rice farmers. All above results remain stable after treatment of endogeneity; (2) due to the migration of the rural labor, the horizontal division of labor is expressed as changes in the structure of labor and cultivation within the household which has changed. To achieve economies of scale, farmers increase specialization in production, resulting in reducing marginal cost and application of fertilizer; (3) the vertical division of labor is expressed in the adoption of external socialized services by farmers, which improves the land resource endowment of fragmentation and hydraulic conditions. Thus, it creates a good environment for fertilizer application to improve its application efficiency, which in turn promotes fertilizer reduction by farmers. Based on this, this paper proposes that the government should motivate farmers to deepen their participation in the horizontal and vertical division of labor. Meanwhile, it is also necessary to improve the agricultural specialization continuously and further promote the development of socialized services market.

AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015

Abstract. Rice is the most important staple food in Asia. However, high-spatiotemporal-resolution rice yield datasets are limited over this large region. The lack of such products greatly hinders studies that are aimed at accurately assessing the impacts of climate change and simulating agricultural production. Based on annual rice maps in Asia, we incorporated multisource predictors into three machine learning (ML) models to generate a high-spatial-resolution (4 km) seasonal rice yield dataset (AsiaRiceYield4km) for the 1995–2015 period. Predictors were divided into four categories that considered the most comprehensive rice growth conditions, and the optimal ML model was determined based on an inverse probability weighting method. The results showed that AsiaRiceYield4km achieves good accuracy for seasonal rice yield estimation (single rice: R2=0.88, RMSE = 920 kg ha−1; double rice: R2=0.91, RMSE = 554 kg ha−1; and triple rice: R2=0.93, RMSE = 588 kg ha−1). Compared with single rice from the Spatial Production Allocation Model (SPAM), the R2 of AsiaRiceYield4km was improved by 0.20, and the RMSE was reduced by 618 kg ha−1 on average. In particular, constant environmental conditions, including longitude, latitude, elevation and soil properties, contributed the most (∼ 45 %) to rice yield estimation. For different rice growth periods, we found that the predictors of the reproductive period had greater impacts on rice yield prediction than those of the vegetative period and the whole growing period. AsiaRiceYield4km is a novel long-term gridded rice yield dataset that can fill the unavailability of high-spatial-resolution seasonal yield products across major rice production areas and promote more relevant studies on agricultural sustainability worldwide. AsiaRiceYield4km can be downloaded from the following open-access data repository: https://doi.org/10.5281/zenodo.6901968 (Wu et al., 2022).

Mixing effects and yield performance of rice varietal mixture (VarMix) in selected areas in the Philippines

Research around the world has established the good performance of the varietal mixture (VarMix) approach in mitigating the ill effects of biotic stresses on various crops. Thus, the efficiency of this approach against abiotic stresses is also being studied. In the Philippines, the evaluation of VarMix is limited especially in large rice areas that experience water scarcity and unpredictable occurrence of pests and diseases. In this study, yield performances of 12 VarMix combinations and their corresponding single varieties were evaluated in 12 environments nationwide that suffer from water scarcity. VarMix ADCEF yielded highest at 7.7t/ha. Analysis of mixing effects showed that 10 of the 12 combinations gained positive MErelml mean ranging from 0.25 to 17.37%. This shows that their mean yield was higher by 0.25% to 17.37% than the yield mean of their single component varieties in different environments. VarMix ABCE achieved the highest MErelml value of 175.86% in Carmen, Bohol with a 17.37% mean. ABCE also had positive MErelml (1.44% to 19.76%) in Agusan del Norte; Aurora; Nabonton, Ligao, City Albay; Batac City, Ilocos Norte; Iloilo; Nueva Ecija; PhilRice Batac, and PhilRice Bicol. Further, ABCE had the highest positive MEmaxml value of 100.79% (found in Bohol), indicating that its yield was 100.79% higher than the maximum yield of its corresponding variety. Batac City and Iloilo likewise had positive MEmaxml of 1.40% and 0.90%. VarMix combinations BDE, AFE, ABCEF, and ABCDEF reached higher D(x)ml with the same mean values of 0.7 across the 12 locations. The highest D(x)ml of 1.9 was found in BDE and BCD. Based on the static measure of all genotype stability, BDE and single variety NSIC Rc 298 attained small �!i values of 2.0, where the lower the value of variability the more stable they were. In the analysis on responsiveness to environmental productivity, ADCEF was the most responsive and the largest stability (bi) value of 1.2 followed by ACDE, ABC and AFE with 1.1; bi >1 means they have higher responses to a given environment. VarMix combinations that showed yield increase through the mixing effects analysis in actual field conditions will facilitate the selection of elite VarMix for future deployment in areas with similar environment. Results from this study highlighted the potential of VarMix as a stop-gap approach for increasing yield in rice production areas with limited source of water and unpredictable occurrences of pests and diseases.

Pengolahan Sekam Bakar Pada Kelompok Pecinta Lingkungan Kecamatan Bantan Kabupaten Bengkalis

Bantan District is one of the rice-producing areas in the Bengkalis archipelago, with an area of 49,500 (ha), an area planted with rice of 1,425 (ha) and a total harvest of 5,785 tons based on data from the Bengkalis district agriculture service in 2012. So that the potential for grain or morning husks produced is also very large and so far only sold in the form of ordinary husks, this should be used as roasted husks which have a higher selling value. For this reason, coaching is needed in making roasted husks so that it becomes a product that has a high selling value, because the market is also high in Bengkalis. This roasted husk can be used as a mixture of soil in polybags so that it is fertile and loose. As well as used for plantation crops in the fields.

Potential and limitations of crowdsourced data for high-resolution rice mapping in Madagascar: The importance of representation

Given the projected population growth and rice consumption in Madagascar, assessing its rice production potential is important. Existing spatial representations of rice are characterized by coarse resolutions and diverge widely. Mapping rice production based on remote sensing offers a potential solution to this problem but its application in Sub-Saharan Africa is hindered by scarce ground truth data and a complex array of cropping systems. While crowdsourcing initiatives provide data that could in principle be used to overcome this hurdle, their usefulness in rice mapping is hitherto underexplored. Here, we investigate the usefulness of crowdsourced data to enhance the mapping of rice production areas in the data-poor context of Madagascar. At present, dedicated crowdsourcing initiatives only limitedly cover rice but do provide sufficient information to build a dataset of data points characterizing a No-Rice class. On the other hand, geotagged pictures from social media, which do document rice production areas, predominantly represent 'scenic' fields appealing to social media contributors, i.e. irrigated, intensive rice production systems. The archetypal nature of these data leads to distinct feature signatures. Consequently, these data could be successfully used for rice mapping in the Lake Alaotra basin, which is characterized by the type of intensive rice cropping that is well-represented by the crowdsourced data. However, it was not possible to construct an accurate map of rice production in the greater Antananarivo region or at the national scale using crowdsourced data as they do not sufficiently represent the present variety in rice production systems. The value of crowdsourced data thus depends on the representativeness of the data, which in turn depends on the application and can be improved through applying a probabilistic sampling design. Our study also highlights the need for data representation checks prior to data augmentation or classification for future applications wanting to construct credible maps using crowdsourcing within their sampling design for which we present a multi-faceted evaluation framework.

Learning from parents and friends: The influence of intergenerational effect and peer effect on farmers' straw return

Straw returning to the field is considered a sustainable agricultural production mode that can be applied on a large scale. However, Straw returning to the field of Chinese farmers has the characteristics of “high willingness, low behavior”. Based on the survey data of 540 farmers in the main rice-producing areas of Sichuan Province, from the perspective of the social network, this study theoretically analyzed the impact of the two most important roles in the farmers' social network - parents and friends, that is, intergenerational effect and peer effect on farmers' straw returning. The probit model was used for empirical testing, and further discussed the difference between the two effects under different natural resource endowment constraints. The results showed that: (1) Among the sample farmers, 61.5% of them used straw returning. (2) The intergenerational effect has an inhibitory effect on the straw returning to the field, and the peer effect has a promoting effect on the straw returning to the field, and the effect of the peer effect is greater than that of the intergenerational effect. (3) Under the constraints of natural resource endowments such as land topography, land area, and home location, the influence of generational effect and peer effect on straw return was different for different farmers. The findings of the study can help improve the policy of integrated straw utilization and promote cleaner rice production.

Genotype x environment interaction for yield of rice hybrids and inbred varieties in Venezuela

Performance tests in multiple locations are essential to study the genotype x–environment interaction, as well as to identify superior genotypes and testing locations. The objective of this study was to evaluate the adaptability and stability of rice hybrids and inbred varieties for grain yield. Six experiments were conducted in rice-producing areas of Venezuela using a randomized complete block design during the dry season of 2015-2016. The ANOVA detected significant differences for genotype, location, and genotype-by-location interaction, highlighting the hybrid by location interaction. The Lin and Binns model identified the hybrids ‘RHA-180’ and ‘HL23035H’ and the ‘Soberana Fl’ variety as adapted and stable. In the GGE biplot model, the first components were significant, and together explained 82 % of the total variability. The hybrids ‘RHA-180’ and ‘HL23035H’ were identified as adapted and stable, whereas the ‘RHA-180’ hybrid was considered the “ideal genotype”. The varieties ‘Soberana Fl’ and ‘SD-20A’ displayed high performance and intermediate stability. The two mega-environments differed by having the best performing genotypes ‘RHA-180’ and ‘Soberana Fl’. Plot 199 was the most representative locality to evaluate hybrids and varieties, whereas the INIA Guárico location discriminated better the rice genotypes. Both models coincided regarding the identification of adapted and stable hybrids and varieties in Venezuela. Nonetheless, while it was easy and efficient to apply the Lin and Binns model, the SREG model was more detailed, effective, and informative.

Indonesian Territory Clustering Based On Harvested Area and Rice Productivity Using Clustering Algorithm

Rice (Latin: Oryza sativa) is one of the most important cultivated plants in civilization. This plant is the main commodity for almost all Indonesian people. Indonesia is in third place as the largest rice producing country in the world. However, based on data from the Statistics Indonesia, Indonesia will still import rice until 2022. The transfer of paddy fields is one of the reasons why Indonesia is still importing rice to this day. Many lands that used to be paddy fields have turned into airports, industrial land, housing, and so on. Rice production is one of the important topics to be discussed in order to develop rice production in areas that are still relatively low. The purpose of this research is to classify cities/regencies in Indonesia based on rice production data in 2021. In this study, three clustering methods were used, namely, Partitioning Around Medoid (PAM), Clustering Large Applications (CLARA) and Fuzzy C-Means (FCM). Then the three methods are compared based on their silhouette coefficient values. The best obtained method is FCM method with two clusters and a silhouette value of 0.828. Results clustering with the best method is used as a reference in making maps clustering. Areas that are still relatively low are expected to increase rice productivity.

Pemanfaatan Machine Learning dengan Algoritma X-Means untuk Pemetaan Luas Panen, Produktivitas, dan Produksi Padi

Rice plants are essential for the world, especially Indonesia because it is a rice-producing plant that is useful as a staple food for its people. A decreased harvest area, production, and rice productivity can affect food availability. Therefore, this research aims to classify and map the harvested area, production, and productivity of rice in Indonesia based on each province. The research data used in this paper is data on the harvested area (ha), production (tons), and rice productivity (Ku/ha) by Provinces in Indonesia for 2020-2022 obtained from the Indonesian Central Bureau of Statistics website. In this study, the algorithm used is X-Means Clustering with the help of the Rapid Miner application. The results of this study are in the form of grouping or mapping of harvested area, production, and productivity of rice, divided into 3 (three) regions, including 1. Harvested Area (divided into five groups: Very high Harvested Area consists of 3 provinces, High Harvested Area consists of 1 province, Medium Harvest Area consists of 3 Provinces, Low Harvest Area consists of 8 Provinces, and Very low Harvest Area consists of 19 Provinces 2. Rice Production Area (divided into five groups: Very high rice production consists of 3 provinces, Rice production High rice production consists of 1 province, Medium rice production consists of 3 Provinces, Low rice production consists of 8 Provinces, and Very low rice production consists of 19 Provinces 3. Regions of Rice Productivity (divided into five groups: Very high rice productivity consists of 6 provinces, High Rice Productivity consists of 13 provinces, Medium Rice Productivity consists of 7 Provinces, Low Rice Productivity consists of 4 Provinces, and Very Low Rice Productivity consists of 4 Provinces. This can be information for the Indonesian government, especially for the respective provincial governments, to be able to maintain the harvested area, production, and productivity of rice in Indonesia to remain stabel.

Identification of Paddy Rice Diseases Using Deep Convolutional Neural Networks

In modern digital agricultural applications, automatic identification and diagnosis of plant diseases using artificial intelligence is becoming popular and widespread. Deep learning is a promising tool in pattern recognition and machine learning and it can be used to identify and classify diseases in paddy rice. In this study, 2 different paddy rice diseases, including rice blast and brown spot, were investigated in the district of İpsala in the province of Edirne between the 2020 and 2021 production seasons by collecting 1569 images. These diseases are very common and important in Edirne province and surrounding rice production areas. Therefore, practical methods are needed to identify and classify these two diseases. A Convolutional Neural Network (CNN) model was created by applying pre-processing techniques such as rescaling, rotation, and data augmentation to the paddy rice disease images. The classification model was created in Google Colab, which is a web-based Python editor using Tensorflow and Keras libraries. The CNN model was able to classify rice blast and brown spot diseases with high accuracy of 91.70%.

Does off-farm employment contribute to chemical fertilizer reduction? New evidence from the main rice-producing area in Jilin Province, China.

Frequent transfer of rural labor to cities in developing countries significantly impacts agricultural production. However, whether off-farm employment can promote chemical fertilizer (CF) reduction is still controversial. This study incorporates business scale (BS) and fragmentation degree of arable land (FDAL) into the theoretical analysis framework, shedding light on regulating effects of arable land resource endowment in the process of off-farm employment which influences CF application under different BS and FDAL scenarios. It also empirically tests the theoretical framework by employing the survey data of 318 rice farmers in Jilin Province. The results indicate that: (1) off-farm employment, in general, promotes the adoption behavior of machinery by farmers, and mechanical tillage can significantly reduce the intensity of CF application. (2) If farmers have large BS and non-dispersed farmland parcels, contiguous cultivation will meet the scale threshold for mechanical farming and obtain economies of scale to reduce the intensity of CF application. (3) If farmers have small BS and dispersed farmland parcels, the scale threshold of mechanical farming cannot be met. In order to stabilize agricultural production, farmers will increase the intensity of CF application. According to the empirical results, we put forward some suggestions.

Impact assessment of adopting improved rice variety on farm household welfare in Ethiopia

This article presents the evaluation of the impact of the adoption of improved rice varieties on yield, commercialization, multidimensional poverty, and subjective poverty using data collected from 594 rice producing smallholder farmers in Ethiopia. We adopted an endogenous switching regression model complemented with a propensity score matching methodology to test robustness and reduced selection bias restricting from both observed and unobserved characteristics. The analysis results are consistent across models, indicating that adoption of improved rice varieties has a robust and positive impact on rice yield, and commercialization. Controlling the variations in household characteristics, the average effect of improved rice varieties adoption on productivity was 0.564 t/ha. Similarly, the econometric result showed that improved rice variety adoption decreased multidimensional poverty and subjective poverty by 1.7% and 12.4%, respectively. The government should work with development partners and NGOs to ease the accessibility and expansion of improved rice varieties in rice producing areas of the country. Therefore, policymakers and development organizations should consider improved rice variety adoption as the main strategy to increase productivity, commercialization, and reduce poverty of the rice farm households. • The study evaluates the impact of improved rice varieties on smallholder farmers' welfare. • The study employed an endogenous switching regression model to analyze data collected from the major rice-producing area of Ethiopia. • Factors influencing the adoption of improved rice varieties identified. • Improved rice variety adoption leads to significant yield and commercialization gains. • Improved rice variety adoption have significant multidimensional poverty and subjective poverty-reducing impacts.

Weather Index Insurance Can Offset Heat‐Induced Rice Losses Under Global Warming

AbstractExtreme heat is one major warming‐related threat to food security and farmers' livelihood, especially in Asia. As an economic adaptation, weather index insurance (WII) can transfer such weather‐related risks, secure farms' incomes, and recover agricultural systems soon after yield losses. However, few relevant studies evaluate its potential in the warming future. Here, we investigated the efficiency of WII to compensate for future heat‐induced yield losses () under SSP1‐2.6, SSP3‐7.0, and SSP5‐8.5 (Shared Socioecomoic Pathways, SSP) in southern China, a typical smallholder‐dominated rice production area in the world. Combining a crop model with machine learning, we found that heat‐induced rice vulnerability showed large spatial variability. Rice vulnerability and warming scenarios co‐determined future and economic losses at the county scale, with more significant losses in the mid‐far future (2051–2100) than in the near future (2021–2050). Compared with the historical period (1961–2010), will increase approximately by up to 5% under SSP1‐2.6, 18% under SSP3‐7.0%, and 26% under SSP5‐8.5. As an ex‐ante strategy, county‐specific WII will effectively improve (by up to 13%) and stabilize (by up to 36%) farmers' incomes, even though the pure premium rate will increase by 10% at 2050 and by 30% at 2100. Overall, our study demonstrates that WII is an effective economic adaption in the future for assisting smallholder livelihood in a developing country. WII has the potential to be applied in other countries for other crop failures to mitigate climate‐related risk for global food systems in the future.

Agricultural Economic Resilience in the Context of International Food Price Fluctuation—An Empirical Analysis on the Main Grain–Producing Areas in Northeast China

In the context of international food price fluctuations, agricultural economic development is usually subject to large shocks, which also leads to clear spatial–temporal differentiation in regional agricultural economic resilience. However, there is a paucity of retrievable studies on the evolutionary process of agricultural economic resilience and its impact mechanisms. Taking the impact of and disturbance caused by international food price fluctuations on the agricultural economy from 2005 to 2021 as the research background, this paper adopts the economic resilience analysis framework and selects gross agricultural output value indicators to measure the agricultural economic resilience of 12 cities in the main grain-producing areas in Northeast China–Sanjiang Plain and Songnen Plain. At the same time, the regression model is used to further analyze the spatial and temporal changes of agricultural economic resilience and the main influencing factors. Based on the results of the research, it can be concluded that: ① international food prices exert different degrees of influence on agricultural economic resilience at different stages. From 2005 to 2013, the agricultural economic resilience of the two plains changed less, and the resilience capacity performed better in both the period of falling and rising international food prices, while from 2014 to 2021, the agricultural economic resilience of the two plains changed more due to the great impact of international food price fluctuations, with a steep decline in resilience capacity in the period of falling international food prices from 2014 to 2017, followed by a rapid rebound between 2018 and 2021. ② There are differences in the resilience capacity of the agricultural economy in the main maize- and rice-producing areas. Although it is a fact that agricultural economic development in the two major plains is faster than the national average in most periods, the comparison reveals that the main corn-producing area (Songnen Plain) is more volatile and its resilience capacity is relatively weak in both periods of falling and rising food prices, while the main rice-producing area (Sanjiang Plain) is less volatile, indicating its relatively strong agricultural economic resilience capacity. ③ Factors affecting agricultural economic resilience behave differently over the three cycles. During the period of rising international food prices, indicators reflecting agricultural production capacity have a greater impact on agricultural economic resilience, while during the period of falling international food prices, indicators reflecting agricultural input intensity and agricultural support capacity have a greater impact on agricultural economic resilience.

Fungi associated with rice sheath rot in Lampung, Indonesia

Rice sheath rot caused by some pathogens. It occurs in the upper leaf sheath that wraps the rice panicle, its major features are rotting, discoloration, sometimes affecting rice grain production. Lampung is an important rice-producing area in Indonesia. Currently, rice sheath rot in the area is reportedly caused by Fusarium sulawesiense and Fusarium hainanense. This study aimed to identify the rice sheath rot pathogen accurately by sampling locations at varying altitudes, plant ages, and varieties in Lampung Sampling was conducted in Lampung, infected plants were collected and the pathogen isolates were molecularly characterized on the basis of DNA sequence data for the internal transcribed spacer and translation elongation factor 1-α. Pathogenicity test results showed that 16 fungal isolates caused rice sheath rot. These isolates were identified as Sarocladium oryzae, Fusarium bubalinum, F. hainanense, Setophoma poaceicola, Curvularia geniculata, and Alternaria padwickii. This study is the first to report that S. poaceicola is a pathogen of rice sheath rot.

A Review of Vector-Borne Rice Viruses.

Rice (Oryza sativa L.) is one of the major staple foods for global consumption. A major roadblock to global rice production is persistent loss of crops caused by plant diseases, including rice blast, sheath blight, bacterial blight, and particularly various vector-borne rice viral diseases. Since the late 19th century, 19 species of rice viruses have been recorded in rice-producing areas worldwide and cause varying degrees of damage on the rice production. Among them, southern rice black-streaked dwarf virus (SRBSDV) and rice black-streaked dwarf virus (RBSDV) in Asia, rice yellow mottle virus (RYMV) in Africa, and rice stripe necrosis virus (RSNV) in America currently pose serious threats to rice yields. This review systematizes the emergence and damage of rice viral diseases, the symptomatology and transmission biology of rice viruses, the arm races between viruses and rice plants as well as their insect vectors, and the strategies for the prevention and control of rice viral diseases.