Climate Sensitivity of Kharif Rice Yield in Manipur, Northeast India

Indian summer monsoon rainfall (ISMR; June–September) has both temporal and spatial variability causing floods and droughts in different seasons and locations, leading to a strong or weak monsoon. Here, the authors present the contribution of all-India monthly, seasonal, and regional rainfall to the ISMR, with an emphasis on the strong and weak monsoons. Here, regional rainfall is restricted to the seasonal rainfall over four regions defined by the India Meteorological Department (IMD) primarily for the purpose of forecasting regional rainfall: northwest India (NWI), northeast India (NEI), central India (CI), and south peninsula India (SPIN). In this study, two rainfall datasets provided by IMD are used: 1) all-India monthly and seasonal (June–September) rainfall series for the entire Indian subcontinent as well as seasonal rainfall series for the four homogeneous regions for the period 1901–2013 and 2) the latest daily gridded rainfall data for the period 1951–2014, which is used for assessment at the extent to which the four regions are appropriate for the intended purpose. Rainfall during July–August contributes the most to the total seasonal rainfall, regardless of whether it is a strong or weak monsoon. Although NEI has the maximum area-weighted rainfall, its contribution is the least toward determining a strong or weak monsoon. It is the rainfall in the remaining three regions (NWI, CI, and SPIN) that controls whether an ISMR is strong or weak. Compared to monthly rainfall, regional rainfall dominates the strong or weak rainfall periods.

Understanding and quantifying long-term rainfall variability at regional scale is important for a country like India where economic growth is very much dependent on agricultural production which in turn is closely linked to rainfall distribution. Using machine learning techniques viz., cluster analysis (CA) and principal component analysis (PCA), the spatial and temporal rainfall patterns over the meteorological subdivisions in India are examined. Monthly rainfall data of 117 years (1901–2017) from India Meteorological Department over 36 meteorological subdivisions in India is used in this study. Using hierarchical clustering method, six homogeneous rainfall clusters were identified in India. Among the rainfall clusters, Group 1 had 30% dissimilarity with Groups 2, 3, and 4 while Group 5 and Group 6 are highly dissimilar (more than 90% dissimilarity) with the rest of the groups. Rainfall seasons in each group were further classified into dry, wet, and transition periods. The duration of dry period is smaller in group which consists of subdivisions from southern part of the country. The transition period between dry and wet period was found to be smaller for subdivisions in the coastal region. Both CA and PCA showed high rainfall variability in Groups 5 and 6, which comprise subdivisions from north east, Kerala, Konkan, and costal Karnataka and low rainfall variability in Groups 1 and 2 which comprise subdivisions from east, north, and central part of the country. Strong negative trend in annual and Indian summer monsoon rainfall is seen in northeast India and Kerala while positive trend is observed over costal Karnataka and Konkan region. The negative trend in post monsoon rainfall particularly over the peninsular and northeast India indicates weakening of northeast monsoon rainfall in the country.

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June–September) and sub-seasonal (July–September, August–September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985–2008) and real-time mode (2009–2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of yield prediction. The findings also recommend that the normal and above normal yields are predicted well in advance using the ERFS forecasts. The outcomes of this study are useful to farmers for taking appropriate decisions well in advance for climate risk management in rice production during different stages of the crop growing season at Kharagpur.

The study examines the impact of agrometeorological conditions, particularly rainfall patterns, on rice yield over the past 30 years in the Anuradhapura Province of Sri Lanka. Anuradhapura is one of the primary rice-growing regions in the country. Long-term data on rice yield †and humidity indices were analysed. The island's climate, especially seasonal monsoon rainfall, significantly influences rice production. Changes in monsoon patterns, rainfall regimes, and air temperature directly affect rice yield and food security. Favourable weather conditions for rice cultivation and yield improvement have been identified.

At present, India Meteorological Department (IMD) issues various monthly and seasonal operational forecasts for the south-west monsoon season using models based on latest statistical techniques with useful skill. Operational models are reviewed regularly and improved through in house research activities. For the forecasting of the south-west monsoon season (June – September) rainfall over the country as a whole, a newly introduced statistical ensemble forecasting system is used. In addition, models have been developed for the forecast of the monsoon season rainfall over four geographical regions (NW India, NE India, Central India and South Peninsula) of the country and forecast for the rainfall over the second half of the monsoon season over the country as a whole. Models have also been developed for issuing operational forecast for the monthly rainfall for the months of July, August & September over the country as a whole. Operational forecasts issued by IMD for 2010 south-west monsoon rainfall have been discussed and verified. In addition, the experimental forecasts for the season rainfall over the country as a whole based on bothstatistical and dynamical models received from various climate research institutes within the country other than IMD arealso discussed.
 The operational monthly and seasonal long range forecasts issued for the 2010 southwest monsoon season for the country as a whole were accurate. However, forecasts for the season rainfall over the 4 geographical regions (Northwest India, Central India, Northeast India and south Peninsular India) were not accurate as the forecast for South Peninsular India overestimated the actual rainfall and that for northeast India underestimated the actual rainfall. The experimental forecasts for the season rainfall over the country as whole from various climate research institutes within the country showed large variance (91 % - 112% of LPA).

Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel

A study is undertaken to find out characteristic features of relationship of the low pressure system (LPS) over the Bay of Bengal and adjoining land regions with the rainfall over different meteorological sub-divisions of India during summer monsoon season (June-September). For this purpose, rainfall over 35 meteorological sub-divisions in India and LPS days over west central (WC) Bay, northwest (NW) Bay, northeast (NE) Bay, Bangladesh (BDS), Gangetic West Bengal (GWB), Orissa, north coastal Andhra Pradesh (NCAP), east Madhya Pradesh and Chattisgarh (EMPC) and Jharkhand (JKD) during different monsoon months and the season as a whole over a period of 18 years (1982-1999) are analysed. There is large month to month variation in the impact of the LPS on the sub-divisional monsoon rainfall over India. However, the results presented in the study including developed correlation maps may be helpful to predict 24 hours rainfall based on the location of the LPS and associated monsoon trough.
 
 The frequent development and persistence of LPS over NW Bay are favourable for higher seasonal monsoon rainfall over east central India. The development and persistence of LPS over WC Bay adversely affect the seasonal rainfall over this region. On the other hand, the frequent development and persistence of LPS over WC Bay and its subsequent westward movement across NCAP are favourable for higher seasonal rainfall over the peninsular region excluding west coast. The seasonal rainfall over northwest India decreases with increase in LPS days over EMPC. The seasonal rainfall over west central India, northeast India and west coast are not significantly related with the number of LPS days over the regions under consideration.

Long period June-to-September total rainfall series are vital for the study of summer monsoon/climatic variability over the Indian region. Owing to high spatial variability in rainfall, the representa tiveness of the summer monsoon rainfall series for the entire country is limited. In order to develop an effective system for monsoon rainfall studies, the country has been divided into six zones, named as North West India (NWI), North Central India (NCI), North East India (NEI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI). Fluctuation, empirical orthogonal function (EOF) and cluster analyses of the summer monsoon rainfall of the period AD 1871-1984 of 306 raingauges are carried out for this classifi cation. Updating the different zonal rainfall series on a real-time basis from all the gauges is a difficult task. In the present study, an objective technique is applied to select a subset of gauges whose mean showed the highest correlation with the all-gauges mean series; a total of 116 raingauge stations (19 for NWI, 27 for NCI, 15 for NEI, 18 for WPI, 14 for EPI and 23 for SPI) maintained by the India Meteorological Department (IMD) have been identified for updating the different zonal series. The different zonal series are extended as far as possible backward (prior to AD 1871) by applying an objective technique to the available gauges. The NEI and the EPI series are extended back to 1848, the NWI to 1844, the NCI to 1842, the WPI to 1817 and the SPI to 1813. Though of limited practical value, the all-India series is widely used in studies of long-range monsoon forecasting, teleconnections and large-scale climate dynamics. The all-India monsoon rainfall series from AD 1871 onwards is therefore calculated from the area-weighted mean of the six zones. The series is also extended back to AD 1813 by applying the objective technique on the available gauges. The different recon structed series for the longest instrumental period are reported.

Rice responses to soil management in a rice-based cropping system in the semi-arid tropics of southern Lombok, Eastern Indonesia

Preliminary and final long range forecast for seasonal monsoon rainfall over India

<p>Climate change, manifested as temperature rise and rainfall change, will pose significant challenges to rice farmers, leading to a possible rice shortage under a changing climate. This research aims to understand the impacts of climate variability and change on rice production through the rest of this century using Representative Concentration Pathway (RCP) scenarios, and combination of statistical and system dynamic modelling. The area of study is West Nusa Tenggara, Indonesia. Wetland and dryland farming types are assessed separately because they have different rice varieties and different agricultural practices. Overall, the research seeks to answer the question: How will climate change and climate variability affect rice production? Additional questions investigated are (1) What are the most significant supply uncertainties associated with a changing climate? and (2) What are possible solutions for reducing the impacts of climate change on rice production?. To answer these research questions, this study deals with three main research areas. First, based on observed data (1976-2011), this study developed regression-based statistical models in understanding the impacts of climate change on rice yield in West Nusa Tenggara. Statistical models find that the negative impacts of increased minimum temperature on rice yield are statistically significant. By contrast, the effects of maximum temperature on rice yield are not statistically significant. A key reason for this is that the highest maximum temperature (32⁰C) in the observed period (1976-2011) was lower than 35⁰C, a rice threshold for maximum temperature. By 2090 (2077-2100), rice yield in wetland and dryland is projected to decrease by about 3% (RCP2.6 scenario), 4% (RCP4.5 scenario), 5% (RCP6.0 scenario) and 14% (RCP8.5 scenario). Second, a system dynamics model was developed to assess the impacts of climate change on three issues including rice yield, harvested areas and rice production by 2090 (2077-2100). After embedding statistical models and estimating the impacts of maximum temperature on rice yield based on existing studies, the impacts of climate change on rice yield are projected. The system dynamics model is also equipped by August SOI to estimate the impacts of climate change on the timing of monsoon onset i.e the beginning of planting seasons. For assessing harvested areas under a changing climate, the system dynamics model is equipped by a mathematical relationship between seasonal rainfall and harvested areas. Because the system dynamics model includes the impacts of high maximum temperature, the projected loss of rice yield in wetland and dryland is relatively higher compared to that in statistical models. It is projected that rice yield loss will be about 3% (RCP2.6 scenario), 6% (RCP4.5 scenario), 10% (RCP6.0 scenario) and 23% (RCP8.5 scenario) by 2090 (2077-2100). Likewise, rice production loss in wetland and dryland is projected to be about 1% (RCP2.6 scenario), 2% (RCP4.5 scenario), 7% (RCP6.0 scenario) and 19% (RCP8.5 scenario) by 2090 (2077-2100). The projected loss of rice production is relatively lower than rice yield loss as wetland harvested areas are projected to experience a slight increase about 3% by 2090 (2077-2100) under a changing climate. This also means that the ranking of the impacts of climate change from the most significant to the least significant is its impact on rice yield, rice production and harvested areas. Third, policy options in overcoming the impacts of climate change on rice production are assessed. This study suggests that research on finding rice varieties with three main traits: heat tolerance, short growth duration and high yield is key to balance rice demand and rice supply in West Nusa Tenggara by 2090 (2077-2100). A failure to improve rice yield in such ways is likely to lead to significant reductions in rice supply in the face of climate change. This study makes theoretical contributions, including the development of statistical models for understanding the impacts of climate change on rice yield and a causal system for investigating the impacts of climate change on rice yield, rice production and harvested areas. Again, the combination of statistical and system dynamics modelling simultaneously investigates the impacts of climate change on rice yield, rice production and harvested areas. This means that this study provides a more holistic view of the impacts of climate change compared to existing studies. This study also offers practical contributions, advising that declining rice research should be avoided under a changing climate, and suggesting that farming intensification (more climate-resilient rice varieties) is more effective than farming extension (area expansion) in sustaining rice production under a changing climate. Again, research on developing more resilient-climate rice varieties is possible as projected rice yield in sustaining rice production by 2090 (2077-2100) is similar to rice’s yield potential.</p>

<p>Climate change, manifested as temperature rise and rainfall change, will pose significant challenges to rice farmers, leading to a possible rice shortage under a changing climate. This research aims to understand the impacts of climate variability and change on rice production through the rest of this century using Representative Concentration Pathway (RCP) scenarios, and combination of statistical and system dynamic modelling. The area of study is West Nusa Tenggara, Indonesia. Wetland and dryland farming types are assessed separately because they have different rice varieties and different agricultural practices. Overall, the research seeks to answer the question: How will climate change and climate variability affect rice production? Additional questions investigated are (1) What are the most significant supply uncertainties associated with a changing climate? and (2) What are possible solutions for reducing the impacts of climate change on rice production?. To answer these research questions, this study deals with three main research areas. First, based on observed data (1976-2011), this study developed regression-based statistical models in understanding the impacts of climate change on rice yield in West Nusa Tenggara. Statistical models find that the negative impacts of increased minimum temperature on rice yield are statistically significant. By contrast, the effects of maximum temperature on rice yield are not statistically significant. A key reason for this is that the highest maximum temperature (32⁰C) in the observed period (1976-2011) was lower than 35⁰C, a rice threshold for maximum temperature. By 2090 (2077-2100), rice yield in wetland and dryland is projected to decrease by about 3% (RCP2.6 scenario), 4% (RCP4.5 scenario), 5% (RCP6.0 scenario) and 14% (RCP8.5 scenario). Second, a system dynamics model was developed to assess the impacts of climate change on three issues including rice yield, harvested areas and rice production by 2090 (2077-2100). After embedding statistical models and estimating the impacts of maximum temperature on rice yield based on existing studies, the impacts of climate change on rice yield are projected. The system dynamics model is also equipped by August SOI to estimate the impacts of climate change on the timing of monsoon onset i.e the beginning of planting seasons. For assessing harvested areas under a changing climate, the system dynamics model is equipped by a mathematical relationship between seasonal rainfall and harvested areas. Because the system dynamics model includes the impacts of high maximum temperature, the projected loss of rice yield in wetland and dryland is relatively higher compared to that in statistical models. It is projected that rice yield loss will be about 3% (RCP2.6 scenario), 6% (RCP4.5 scenario), 10% (RCP6.0 scenario) and 23% (RCP8.5 scenario) by 2090 (2077-2100). Likewise, rice production loss in wetland and dryland is projected to be about 1% (RCP2.6 scenario), 2% (RCP4.5 scenario), 7% (RCP6.0 scenario) and 19% (RCP8.5 scenario) by 2090 (2077-2100). The projected loss of rice production is relatively lower than rice yield loss as wetland harvested areas are projected to experience a slight increase about 3% by 2090 (2077-2100) under a changing climate. This also means that the ranking of the impacts of climate change from the most significant to the least significant is its impact on rice yield, rice production and harvested areas. Third, policy options in overcoming the impacts of climate change on rice production are assessed. This study suggests that research on finding rice varieties with three main traits: heat tolerance, short growth duration and high yield is key to balance rice demand and rice supply in West Nusa Tenggara by 2090 (2077-2100). A failure to improve rice yield in such ways is likely to lead to significant reductions in rice supply in the face of climate change. This study makes theoretical contributions, including the development of statistical models for understanding the impacts of climate change on rice yield and a causal system for investigating the impacts of climate change on rice yield, rice production and harvested areas. Again, the combination of statistical and system dynamics modelling simultaneously investigates the impacts of climate change on rice yield, rice production and harvested areas. This means that this study provides a more holistic view of the impacts of climate change compared to existing studies. This study also offers practical contributions, advising that declining rice research should be avoided under a changing climate, and suggesting that farming intensification (more climate-resilient rice varieties) is more effective than farming extension (area expansion) in sustaining rice production under a changing climate. Again, research on developing more resilient-climate rice varieties is possible as projected rice yield in sustaining rice production by 2090 (2077-2100) is similar to rice’s yield potential.</p>

Temperature of the earth’s surface increased over the last few decades because of global warming which effected the hydrological cycle and patterns of precipitation across World. Precipitation is one of the important parameter of water cycle which affects the agriculture by influencing the availability of water resources. The trend as well as pattern of the rainfall disturb significantly because of the climatic variability. This study carried out for finding the decadal changes of seasonal rainfall (monsoon), annual rainfall, and also the seasonality index (SI) along with annual as well as seasonal trend analysis rainfall for all thirty districts of Odisha. Daily rainfall data of thirty districts over 30 (1990–2019) years were collected and converted to annual rainfall and seasonal rainfall (monsoon) data, and was used for the analysis. Decade wise, Angul, Balasore, Dhenkanal, Jagatsinghpur, Jajpur, Kendrapara, Khordha and Puri Districts experienced decrease in average annual rainfall whereas Nawarangpur and Nayagarh Districts detected with increase in average annual rainfall. Decade wise, decrease in average monsoon rainfall is observed for Bhadrak, Bolangir aand Dhenkanal districts where as increase of average monsoon rainfall is observed in Gajapati, Ganjam, Koraput and Nuapada Districts. SI value was found to be lowest (0.76) in Gajapati district whereas maximum value of 1.13 in Bargarh district. Mann–Kendall test was performed for the seasonal as well as monsoon rainfall trend analysis. Amongst all districts, Koraput district experienced significant increasing trend for the annual rainfall. Significant increasing trend of seasonal (monsoon) rainfall observed for Koraput and Gajapati districts. Districts experiencing decreasing trend are critical from water resource management and crop planning view for annual and monsoon rainfall respectively.KeywordsRainfallTrendSeasonality indexWater resources management

Reliable information of rainfall and its variability at finer spatio-temporal scales over the Indian monsoon region is crucial for various applications in hydrology, meteorology, water resource management, and evaluation of high-resolution numerical model outputs. In this study, the Tropical Rainfall Measuring Mission (TRMM)-based high-resolution multisatellite rainfall products namely, TRMM Multisatellite Precipitation Analysis (TMPA)-3B42 and Global Satellite Mapping of Precipitation (GSMaP) are evaluated against recently released gauge-based India Meteorological Department (IMD) gridded data set. Both the research (delayed mode) and near real-time rainfall products of TMPA-3B42 (V7 and RT) and GSMaP (MVK and NRT) are evaluated over India at a daily scale for the peak southwest monsoon months of July–August. Two study periods (2000–2010 and 2009–2013) are used to accommodate the analyses based on data availability. Comparison of TMPA-3B42 (V7 and RT) and GSMaP-MVK with IMD gauge-based data for 2000–2010 shows that these data sets are able to represent large-scale monsoon rainfall spatial features, but are having region-specific biases. The interannual variability of monsoon rainfall averaged over India is also reasonably captured. However in terms of biases, TMPA-3B42RT overestimates rainfall by about 21% and GSMaP-MVK underestimates by about 22% at all-India scale. In terms of continuous skill metrics, TMPA-3B42V7 is closer to the IMD gauge-based observations possibly due to gauge adjustments. Categorical verification against gauge-based observations shows that the satellite-based data sets have difficulties in rainfall detection primarily over the northern Jammu & Kashmir regions, northeast India, and southern peninsular India. At subregional scale within India, satellite rainfall estimates show rather lower skill score in rainfall identification and larger uncertainty in rainfall estimation over the northeast India. Comparison of near real-time products of TMPA and GSMaP for July–August months of 2009–2013 shows that TMPA-3B42RT overestimates and GSMaP-NRT underestimates rainfall over most parts of India. Even though TMPA-3B42 (V7 and RT) and GSMaP-MVK rainfall data sets show similar diurnal variations over the Indian monsoon region, TMPA-3B42RT (GSMaP-MVK) shows larger (smaller) magnitude in diurnal cycle as compared to TMPA-3B42V7. Results from different skill metrics suggest that both research and near real-time versions of TMPA products can be used with relatively higher confidence as compared to GSMaP over the Indian region during peak monsoon period.

This study addresses the impact of projected changes to northeast monsoon on rice yield during rabi season (September–December) in Tamil Nadu by using a three‐step approach. First, coarse‐resolution global climate models that realistically capture the mean monsoon characteristics were selected. Second, lateral and boundary conditions taken from selected global models’ projections are employed to run a high‐resolution regional climate model. Third, climate variables from regional model being fed into panel data regression model. For different scenarios and for mid and end of century projections, in conjunction with projected rainfall, a comprehensive assessment is carried out to underscore the sensitivities of maximum and minimum temperatures under different stages of rice production, viz. vegetative, reproductive and maturity phases, and to the concept of growing degree days (GDD, cumulative heat effect). Irrespective of scenarios, in response to an increase in projected monsoon rainfall and surface temperature conditions, the regression model estimates an increase of rice yield of about 10–12% by mid‐century and 5–33% by the end of the century. In the regression model, the baseline coefficients were estimated from observed rainfall and temperature available from India Meteorological Department (IMD). The projected changes in rice yield, however, remain unchanged for baseline coefficients estimated from regional climate model outputs (forced by reanalysis products) rainfall and temperature. The robust results obtained here provide confidence to the findings.