Rice Area Research Articles

Pengaruh Luas Lahan Panen Padi dan Tenaga Kerja Sektor Pertanian terhadap Produksi Padi di Provinsi Jambi

This research aims to determine the influence of rice harvest area and agricultural sector workforce on rice production in Jambi Province. The type of data collected is secondary data sourced from Jambi in the Jambi Province Central Statistics Agency (CSA) figures in the form of Gross Regional Domestic Product (GRDP) in the agricultural sector of Jambi Province. The research results show that the area of rice harvested land has a significant and positive effect, while agricultural sector labor does not have a significant and negative effect on rice production during the 2010-2022 period.

Effects of Waterlogging on Rice Growth at Jointing–Booting Stage

The rice waterlogging stress test was conducted at the experimental base of the College of Agriculture, Yangtze University, using Yangxian You 418 as the test subject, in order to investigate the impact of waterlogging on rice growth during the period from July to August each year. Six waterlogging stress tests with different waterlogging depth (1/4 plant height (1/4PH), 2/4 PH, and 3/4 PH) and duration (5 d and 7 d) were set up at the jointing–booting stage of rice (T1: 1/4 PH, 7 d; T2: 2/4 PH, 7 d; T3: 3/4 PH, 7 d; T4: 1/4 PH, 5 d; T5: 2/4 PH, 5 d; T6: 3/4 PH, 5 d;) with shallow water irrigation (CK) as control. The plant height, population leaf area, above-ground dry matter, and the yield of rice were measured. The correlation between the waterlogging depth and rice yield reduction was analyzed, and the flood disaster threshold index of rice was established. The results showed that at the end of stress, the plant height of all waterlogged treatments exceeded CK, and the plant height of T3 and T6 treatments significantly increased by 31.90% and 15.93%, respectively. The leaf area of rice treated with T1, T3, T4, and T5 was higher than CK (p < 0.05), and the above-ground dry matter of rice treated with T2, T3, T4, T5, and T6 was higher than CK (p < 0.05). When normal irrigation was restored to the maturity stage, the plant height of all rice treated with waterlogging was still higher than CK (p < 0.05). However, as the degree of waterlogging increased, rice yield decreased significantly, with a notable reduction of 31.68% observed in the T3 treatment compared to CK. Assuming a drainage index based on a 20% decrease in rice yield, it is imperative that the ratio of flooded depth to plant height remains below 37% when waterlogging persists for 7 days in rice cultivation. These research findings offer crucial scientific insights for implementing effective drainage management measures during flood disasters in rice paddies.

Multidimensional analysis for assessing sustainability determinants of rice farming in Siak, Riau

Abstract This study was conducted in five villages in Bunga Raya District (Kemuning Muda, Bunga Raya, Tuah Indrapura, Jayapura, and Buantan Lestari), the rice production centers of Siak Regency, Riau. The research location was selected based on the largest percentage of rice field area. The research was conducted from January to July 2019. This research aims to determine sustainable lowland rice farming management variables for future lowland rice farming development in Bunga Raya District, Siak Regency. Purposive sampling was used to choose the 82 sample farmers, who were selected using the Slovin equation. The respondents are selected with a margin of error of 7% of the total population of farmer families. Data was gathered through the use of interview techniques, questionnaires, and field observations. Multi-dimensional scaling (MDS) was used to analyze sustainable rice farming management factors after updating the Rapfish program. According to the findings of the sustainability study of 42 studied variables, 12 of them show sensitivity. Five of the ten stakeholder needs attributes were examined to provide sensitivity. The combined analysis of current prospective sustainability with stakeholders yielded five attributes determining factors for the sustainability of lowland rice farming in Bunga Raya: land conversion, participation in training/extension services, pesticide use, agro-tourism potential, and farmer capital availability. Another recommendation that policy makers need to consider, which is no less important, is to always preserve the rice field ecosystem by developing natural and human resources, so that increasing the welfare of farmers and increasing regional income can be achieved.

Assessing climate resilience in rice production: measuring the impact of the Millennium Challenge Corporation’s IWRM scheme in the Senegal River Valley using remote sensing and machine learning

Satellite remote sensing (RS) and machine learning can be combined to develop methods for measuring the impacts of climate change on biomass and agricultural systems. From 2015 to 2023, we applied this approach in a critical earth observation-based evaluation of the Irrigation and Water Resources Management component of the Millennium Challenge Corporation’s Senegal Compact. This project, funded by the United States Agency for International Development (USAID), was implemented in the Senegal River Valley from 2010 to 2015. Utilising these techniques, we successfully mapped rice cultivation areas, deciphered cropping practices, and analysed irrigation systems responses to different climatic conditions. A marked increase in cultivated rice area was found particularly in regions targeted by the project intervention. This is despite prolonged drought conditions which underscores a significant climate adaptation benefit from these irrigation works. We observed a notable dip in rice cultivation area in 2020, possibly due to the COVID-19 pandemic, followed by a recovery to pre-pandemic levels in 2023, likely aided by previously funded USAID’s socio-economic resilience programmes in the region. Economic analysis of increased rice yields in the region translates to approximately US$ 61.2 million in market value since 2015, highlighting the economic returns from the project investment. Both the RS data and ground audits identify issues regarding post-project deterioration of irrigation infrastructure, emphasising the need for long-term maintenance of irrigation infrastructure to support climate adaptation benefits arising from irrigation. With a focus on crop irrigation, our findings stress the critical role of climate adaptation interventions for maintaining agricultural productivity in the face of adverse climate shocks. It further highlights the necessity of continuous investment and maintenance for ensuring climate resilient agrifood systems.

Evaluation of the Sragi Secondary Channel of the Kaliwadas Irrigation Area, Pekalongan Regency

The Sragi secondary channel is located in the Kaliwadas irrigation area, Pekalongan Regency has a length of 7,587 m. The position of the channel is in a rice field area which is used to optimize irrigation to support agricultural business productivity. The existing condition of the channel in the form of native soil causes frequent leaks. Technical handling is needed to repair irrigation buildings in the form of a study on the existing condition of the Sragi secondary channel through field surveys and checking physical conditions. Furthermore, identification of the level of damage to irrigation channels is carried out. From the problems that arise at the identification stage, recommendations are then given in the form of proposals for improvements to buildings and irrigation channels. The results of the field survey in the form of network tracing provide data that the level of physical and functional damage to the Sragi secondary channel infrastructure reaches >40%. The condition is categorized as very bad with the classification of severely damaged which requires repair or replacement. Most of the damage in the form of spots or localized occurred on the existing lining of masonry and precast concrete starting from Hm 50+00 to Hm75+68. The recommendations for improvement are in the form of dismantling the existing precast concrete lining according to the point of damage. It is recommended to repair the lining using K-300 precast concrete so that the structure of the irrigation channel is safe against the risk of deformation.

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Spatiotemporal dynamic of rice production and its carbon footprint in Hainan, China: Implications for food security and environmental sustainability.

Rice (Oryza sativa L.) feeds more than half of the global population and faces the critical issues related to food security and environmental sustainability. This study analyzed double rice production data from 2010 to 2020 to assess its spatiotemporal dynamic in food production and carbon (C) footprint in Hainan province, China. The results revealed a 29.5% reduction in rice planting area, leading to a significantly decreased rice self-sufficiency rate from 38% to 33% from 2010 to 2020. During this period, the carbon footprint per unit area (CFa) for early, late, and double rice showed a fluctuating upward trend ranging from 8.1 to 8.4, 8.9 to 9.2, and 17.0 to 17.4 t CO2-eq ha-1, respectively. The total greenhouse gas (GHG) emissions of rice production decreased to around 2 million t CO2-eq, primarily due to reduced planting area. The C sequestration initially increased before decreasing to 1.2 million t C in 2020 at a temporal scale. Spatially, the northeast and southwest regions exhibited ∼70% of the total GHG emissions and ∼80% of C sequestration. The regional C footprint per unit yield displayed less favorable outcomes, with some areas (e.g., Wenchang and Haikou) experiencing emission hotspots in recent years. Higher yield and smaller CFa for Lingao and Tunchang were observed compared to the average between 2010 and 2020. This study provides insights into the spatiotemporal dynamics of double rice production and GHG emissions in Hainan, offering a scientific reference for regional food security and environmental sustainability.

Ideal phenotypic characteristics and parental lines for hybrid rice breeding to cope with global dimming

The global food crisis has intensified with climate change, and hybrid rice (Oryza sativa L.) is expected to play a crucial role in ensuring world food security. This study analyzed the parental origins and variety characteristics of 930 hybrid rice varieties bred in Sichuan Province, a typical low-light region and one of the main production areas for hybrid rice, from 2002 to 2022. The results indicate that these varieties were hybridized using 329 direct female and 539 direct male parents. Notably, the female parent Peiai 64 and the male parents Yuehesimiao and Fenghuazhan demonstrated a higher probability of breeding rice varieties with high yield and good quality. An ideal panicle type should possess more grains, a lower 1000-grain weight, a higher seed setting percentage, and greater grain density. It is recommended to increase the utilization of excellent parents in Sichuan Province, such as parents with ideal panicle type to improve resistance to climate change, particularly global dimming.

Climate-adaptive crop distribution can feed food demand, improve water scarcity, and reduce greenhouse gas emissions

Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China.

Optimum Rice Prediction from Conventional, Neural Network and Hybrid models

The objective of study was to propose the appropriate model for forecasting the area and production of rice crops in Pakistan. The data for the rice area and production was taken for the agriculture statistics from 1947 to 2020 from the official website, Ministry of Finance, Government of Pakistan. The conventional ARIMA methodology was applied initially to forecast the rice area and production by using the proposed ARIMA(1,1,0) model. Then ARIMA, ETS, TBATS, Artificial Neural Network (ANN) and ARIMA-ETS, ARIMA-TBATS and ARIMA-ANN hybrid model were compared. It was observed that ARIMA(1,1,0) model was best for forecasting the production of rice than all other seven models with the lowest value of RMSE = 401.90 (tons) and MAE = 250.32. For forecasting the rice area, the ARIMA-ANN model was selected because it showed the lowest value of RMSE = 121.89 (Ha) and MAE = 85.82 among all the models. Rice area and production was forecast from 2021 to 2030 by the proposed models, which show that the average prediction of rice production is 7879.96 tons while the rice area would be 3010.82 hectare for the next ten years. It shows that area will be increased by 2.11 percent and rice production will be increased by 11.97 percent upto 2030.. KEYWORDS :Forecasting, ARIMA, TBATS, ETS, ANN.

Dinamika Perkembangan Luas Panen, Produksi Dan Produktivitas Padi Sawah Sebelum Dan Sesudah Program Humasa Sebbel Khumaha Heba Di Kabupaten Simeulue

Meeting food needs is a right and is protected by law in Indonesia. The harvested area for lowland rice in Indonesia and the Province of Aceh has fluctuated every year. Simeulue Regency in 2020 has implemented the Humasa Sebbel Khumaha Heba Public Relations Program (Together down to the fields) with the aim of food self-sufficiency in Simeulue Regency. The purpose of this study was to determine the development of harvested area, production, and productivity of lowland rice in Simeulue Regency before and after the Humasa Sebbel Khumaha Heba program. The analytical method used is descriptive comparative based on secondary data obtained from the Agriculture and Food Crops Office of Simeulue Regency and the Statistics Indonesia-BPS in the form of time series from 2018 to 2021. The results of this study are the Humasa Sebbel Khumaha Heba program can increase harvested area and lowland rice production in Simeulue Regency but its productivity continues to decline.

Old Foe, New Host: Epidemiology, Genetic Diversity, and Pathogenic Characterization of Maize Streak Virus in Rice Fields from Burkina Faso

Rice is of critical significance regarding food security worldwide, including in Africa. Only two viruses impacting rice production in Africa have been deeply investigated for decades: the rice yellow mottle virus ( Solemoviridae) and the rice stripe necrosis virus ( Benyviridae). Using viral metagenomics, we aimed at broadening knowledge on interacting communities associated with plants in rice landscapes and exploring the diversity and epidemiological status of viruses circulating in rice fields from Burkina Faso. We performed an epidemiological survey in this country between 2016 and 2019 involving 57 small farmers’ rice fields under two production systems: rainfed lowlands and irrigated areas. More than 2,700 rice samples were collected without regard to disease symptoms following a regular scheme. Wild and cultivated Poaceae (maize and sugarcane) growing in nearby rice fields were also collected. Unexpectedly, metagenomics detected maize streak virus (MSV) ( Geminiviridae) in analyzed rice samples. Further molecular analyses using rolling circle amplification-polymerase chain reaction showed that MSV was widely distributed and highly prevalent in both rainfed lowlands and irrigated rice areas. MSV-A and MSV-G strains were identified. MSV-G, exclusively identified thus far in wild grasses, was the most prevalent strain, whereas MSV-A, known to cause severe symptoms in maize, was sporadically identified. Using infectious clones in experimental conditions, we confirmed the pathogenicity of both MSV strains in rice. Thus, in addition to contributing to the epidemiological surveillance of rice production in Africa, our results illuminate new epidemiological and pathogenic aspects of one of the most studied plant viruses with significant economic consequences in Africa. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change.

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Determinants of Profit Efficiency among Rice Farmers in Kilombero Valley, Morogoro, Tanzania

Farmers in the Kilombero Valley are experiencing a growing trend towards commercialization seen through the expansion in the cultivated land area rather than agricultural intensification. There is a modicum of information on whether the observed increase in commercialization in the area is associated with an increase in profit. This study used the stochastic profit frontier function to estimate the profit efficiency of rice commercializing farmers and the respective determinants. The study used data collected from 377 rice farmers who were selected by using a multi-stage stratified sampling method. Results indicate that rice commercialising farmers' mean profit efficiency level was 75.65%. This indicates that rice farmers can improve their efficiency levels by 24.4% without increasing the level of inputs used. Maximum likelihood estimates indicated that wage, price of fertilizer, rice area in hectares, and production assets value were significant in determining the profit efficiency of rice commercializing farmers in Kilombero. The study concludes that commercializing farmers in the study area was profit efficient. The study recommends that more emphasis on early formal education in farmers’ society be emphasized for the literate group is more profit-efficient than the illiterate. With the present national educational policy to support education, farming households should strategically take advantage of it as education has exhibited a positive impact on their efficiency. Different services (marketplaces and inputs dealerships) in the area should be improved further to create more room for rice farmers to improve their productivity and profitability.

Spatiotemporal variation in rice water requirements and area in the cold rice cultivation region of China: Past and Future

Studying rice water requirements (ETc) and area is crucial for developing irrigation schedules and adjusting crop structures. The cold rice cultivation region is China’s main high-quality rice production base and has the lowest temperature, highest latitude, and greatest sensitivity to climate. However, little research has focused on its past rice distributions and ETc, and its future variations are not yet clear. Past ETc was calculated by the Penman–Monteith and crop-corrected coefficient method, and past rice distribution patterns were analyzed based on the annual paddy rice planting area datasets. Future reference crop evapotranspiration (ET0) was projected by the statistical downscaling model (SDSM) based on the CMIP6 ensemble of daily predictor variables. The food and agriculture projections to 2050 datasets were used to project future rice areas, and the patch-generating land use simulation (PLUS) model simulated rice distribution. Finally, we projected future rice water supply demand. The results showed that ETc showed a decreasing trend, and the total rice area and production increased by 4.52 and 5.49 times from 1990 to 2020. Both in the past and future, rice was densely distributed in and expanded into the Sanjiang and Songnen Plains, and the spatial characteristic of ETc was as follows: the west was larger than the east, the north was larger than the south, and the central was in between. In the four future periods, the total ETc demonstrated an upward trend, increasing by 7.44–21.04, 15.64–34.74, 24.74–45.84, and 16.14–41.14 mm under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively, compared to the historical period. And it is generally ranked as SSP370 > SSP245 > SSP126. Finally, we projected future rice water supply meet the demand caused by rice area expansion.

Assessing paddy methane emissions through the identification of rice and winter crop areas using Sentinel-2 imagery in Korea

The global efforts on reducing methane (CH4) emissions was emphasized in COP 28 and the potential for improved estimation became feasible through bottom-up data acquisition with advanced remote sensing technology. The objectives of this study were to extract summer rice and winter crop cultivation areas based on satellite images and to incorporate into estimating CH4 emissions in South Korea for the year 2020. Satellite images of Sentinel-1 and Sentinel-2 were acquired from European Space Agency. Rice paddy was classified with backscattering coefficient from Sentinel-1 images, while the normalized difference vegetation index from Sentinel-2 images was used to identify winter cropping field. The equation of IPCC guidelines was used to estimate CH4 emissions by incorporating the areas of rice paddy and winter crop extracted with the respective satellite image. National farming statistics were used to determine the scaling factors for paddy organic matter and water management practices. The estimated areas for rice paddy and winter crop cultivation were 712,237 ha and 117,840 ha, respectively. The rice paddy areas were primarily concentrated in the western regions of the Korean peninsula, whereas winter crop cultivation was predominantly found in southern part of the country. The total amount of CH4 emissions was 6272 Gg CO2 eq./yr when considering rice straw and winter cropping practices into estimation (modified Tier 2 method). This represents a 7% increase compared to the method that considered solely the rice straw incorporation (current Tier 2 method). The CH4 emissions per unit area were also 8.82 tons CO2 eq./ha/yr with the modified Tier 2 method, indicating a 10% greater compared to the current Tier 2 method. Substantial CH4 emissions were primarily concentrated in western regions where extensive rice paddy cultivation occurs, while greater CH4 emissions per unit area were predominantly found in southern regions with substantial winter crop cultivation. The study findings hold importance for improving the accuracy of CH4 emissions estimation by employing bottom-up approach that utilizes satellite imagery to assess rice paddy and winter cropping areas. Further study would be needed to incorporate field-based data on rice crop management practices, such as rice straw and water management, to further refine CH4 emission estimation method.

The state and trends of land use development in Krasnoarmeysky district of the Krasnodar territory

The article analyzes the state and trends in the development of land use in the rice-growing zone of Kuban. The lands of Krasnoarmeysky district were chosen as the object of study, since it is the main rice growing area in Krasnodar region. It has been established that rice production makes significant influence to the nature, as it changes and transforms the environment, and especially land resources. An analysis of indicators characterizing the state of the lands in Krasnoarmeysky district was carried out, and a forecast for each of these indicators was calculated here as well. The results of this forecast until 2035 indicate a gradual decrease of agricultural lands (and arable lands) in the area, that can lead to reduction of the resource potential of agricultural production. The dynamics of indicators of the sown area, gross harvest and yield of rice in Krasnoarmeysky region are considered by the authors. It was found that rice production is growing steadily, however, an analysis of changes in the area under crops and the gross yield of rice indicates the insufficient efficiency of the existing technology for cultivating this crop, based on the intensive use of mineral fertilizers. Forecasting shows huge increase of used mineral fertilizers, accompanied by a reduction of organic substances in the soil and deterioration of the environmental situation. In order to ensure the environmental sustainability of Krasnoarmeysky district, the new ecosystem approach to land use in the rice-growing zone has been proposed, including a set of environmental and resource-saving measures. As a result of the proposed measures, the structure of land use, crop rotation patterns and production technology have to be changed, use environmentally friendly directions, and preserve and improve the conditions of land resources.

Modelling the impact of different irrigation regimes and mulching on strawberry crop growth and water use in the arsenic-contaminated Bengal basin

Replacement of water-intensive winter rice with strawberry (Fragaria × ananassa Duch.) may restrict groundwater extraction and improve water productivity and sustainability of agricultural production in the arsenic-contaminated Bengal basin. The potential of strawberry cultivation in terms of yield obtained and water use efficiency need to be evaluated under predominant soil types with mulch applications. Water-driven model AquaCrop was used to predict the canopy cover, soil water storage and above-ground biomass of strawberry in an arsenic-contaminated area in the Bengal basin. After successful calibration and validation over three seasons, AquaCrop was used over a range of management scenarios (nine drip-irrigation × three soil types × four mulch materials) to identify the best irrigation options for a drip-irrigated strawberry crop. The most appropriate irrigation of 176 mm for clay loam soil in lowland and 189 mm for sandy clay loam in medium land rice areas and the use of organic mulch from locally available jute agrotextile improved 1.4 times higher yield and 1.7 times higher water productivity than that of without mulch. Strawberry can be introduced as an alternative crop replacing rice in non-traditional upland and medium land areas of the arsenic-contaminated Bengal basin with 88% lower groundwater extraction load and better economic return to farmers.

Incorporating Multi-Temporal Remote Sensing and a Pixel-Based Deep Learning Classification Algorithm to Map Multiple-Crop Cultivated Areas

The accurate monitoring of crop areas is essential for food security and agriculture, but accurately extracting multiple-crop distribution over large areas remains challenging. To solve the above issue, in this study, the Pixel-based One-dimensional convolutional neural network (PB-Conv1D) and Pixel-based Bi-directional Long Short-Term Memory (PB-BiLSTM) were proposed to identify multiple-crop cultivated areas using time-series NaE (a combination of NDVI and EVI) as input for generating a baseline classification. Two approaches, Snapshot and Stochastic weighted averaging (SWA), were used in the base-model to minimize the loss function and improve model accuracy. Using an ensemble algorithm consisting of five PB-Conv1D and seven PB-BiLSTM models, the temporal vegetation index information in the base-model was comprehensively exploited for multiple-crop classification and produced the Pixel-Based Conv1D and BiLSTM Ensemble model (PB-CB), and this was compared with the PB-Transformer model to validate the effectiveness of the proposed method. The multiple-crop cultivated area was extracted from 2005, 2010, 2015, and 2020 in North China by using the PB-Conv1D combine Snapshot (PB-CDST) and PB-CB models, which are a performance-optimized single model and an integrated model, respectively. The results showed that the mapping results of the multiple-crop cultivated area derived by PB-CDST (OA: 81.36%) and PB-BiLSTM combined with Snapshot (PB-BMST) (OA: 79.40%) showed exceptional accuracy compared to PB-Transformer combined with Snapshot and SWA (PB-TRSTSA) (OA: 77.91%). Meanwhile, the PB-CB (OA: 83.43%) had the most accuracy compared to the pixel-based single algorithm. The MODIS-derived PB-CB method accurately identified multiple-crop areas for wheat, corn, and rice, showing a strong correlation with statistical data, exceeding 0.7 at the municipal level and 0.6 at the county level.

An analysis of long-term and short-term impact of climate change on rice production in India

PurposeClimate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.Design/methodology/approachThis study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.FindingsThe study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.Originality/valueThe present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.