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To clarify the current situation, hotspots, and development trends in the field of rice and climate change topic research, a massive literature dataset were analyzed from the Web of Science database by bibliometric method. The research theme was chosen given the continuous increase of studies related to climatic changes and their consequences to rice. Based on the Web of Science core database, this study analyzed 4170 papers in the field of rice and climate change topic research from 1990 to July 2022, which include 86 highly cited papers and 3 hot papers. Papers were mainly written in English (4157, 99.688%), from 16,363 authors, 4017 organizations, and 129 countries/territories, published in 841 journals and seven book series. The top five Journals are Science of the Total Environment (136, 3.261%), Sustainability (89, 2.134%), Agronomy Basel (81, 1.942%), Agricultural and Forest Meteorology (77, 1.847%), and Climatic Change (74, 1.775%), each published more than 74 papers. Top five countries and regions of People's Republic of China, the USA, India, Australia, and Japan were the major article contributors, each published more than 360 papers. Top five organizations of Chinese Acad Sci, Nanjing Agr Univ, Univ Chinese Acad Sci, Chinese Acad Agr Sci, and Int Rice Res Inst (IRRI) were popular based on contribution of articles more than 133 papers each. Among the all authors, top five authors were Tao Fulu, Pan Genxing, Zhang Zhao, Hasegawa Toshihiro, and Iizumi Toshichika, each published more than thirty papers. All keywords were separated into eight clusters for different research topics. Visualizations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The results will help researchers clarify the current situation in rice and climate change adaptation science but also provide guidance for future research. This work is also useful for student identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

This paper presents the methodology for assessment of drought episodes and their potential effects on winter and spring cereal crops in the Czech Republic (in the text referred to as Czechia). Historical climate and crop yields data for the period of 47 years (1961–2007) have been integrated into an agrometeorological database. The drought episodes were determined by three methods: according to the values of the standardized precipitation index (SPI), percentage of long-term precipitations ( r), and on the basis of the Ped drought index ( Si). Consequently, the combined SPI, Si, and r indices have been used as tools in identification of the severity, frequency, and extent of drought episodes. Additionally, the paper also presents the Si drought index and its potential use for real-time monitoring of spatial extension and severity of droughts in Czechia. The drought risk to crops was analyzed by identifying the relationships between the fluctuation of crop yields and drought index ( Si) based on the multiple regression analysis with stepwise selection. In general, models explain that a high percentage of the variability of the yield is due to drought (more than 45% of yield variance). [ABSTRACT FROM AUTHOR]

Rainfed agriculture plays and will continue to play a dominant role in providing food and livelihoods for an increasing world population. Rainfall analyses are helpful for proper crop planning under changing environment in any region. Therefore, in this paper, an attempt has been made to analyse 16 years of rainfall (1995-2010) at the Daspalla region in Odisha, eastern India for prediction using six probability distribution functions, forecasting the probable date of onset and withdrawal of monsoon, occurrence of dry spells by using Markov chain model and finally crop planning for the region. For prediction of monsoon and post-monsoon rainfall, log Pearson type III and Gumbel distribution were the best-fit probability distribution functions. The earliest and most delayed week of the onset of rainy season was the 20th standard meteorological week (SMW) (14th-20th May) and 25th SMW (18th-24th June), respectively. Similarly, the earliest and most delayed week of withdrawal of rainfall was the 39th SMW (24th-30th September) and 47th SMW (19th-25th November), respectively. The longest and shortest length of rainy season was 26 and 17 weeks, respectively. The chances of occurrence of dry spells are high from the 1st-22nd SMW and again the 42nd SMW to the end of the year. The probability of weeks (23rd-40th SMW) remaining wet varies between 62 and 100 % for the region. Results obtained through this analysis would be utilised for agricultural planning and mitigation of dry spells at the Daspalla region in Odisha, India. [ABSTRACT FROM AUTHOR]

Numerous drought indices have been developed to analyze and monitor drought condition, but they are region specific and limited by various climatic conditions. In southwest China, summer drought mainly occurs from June to September, causing destructive and profound impact on agriculture, society, and ecosystems. The current study assesses the availability of meteorological drought indices in monitoring summer drought in this area at 5-day scale. The drought indices include the relative moisture index (M), the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the composite index of meteorological drought (CIspi), and the improved composite index of meteorological drought (CIwap). Long-term daily precipitation and temperature from 1970 to 2014 are used to calculate 30-day M (M30), SPI (SPI30), SPEI (SPEI30), 90-day SPEI (SPEI90), CIspi, and CIwap. The 5-day soil moisture observations from 2010 to 2013 are applied to assess the performance of these drought indices. Correlation analysis, overall accuracy, and kappa coefficient are utilized to investigate the relationships between soil moisture and drought indices. Correlation analysis indicates that soil moisture is well correlated with CIwap, SPEI30, M30, SPI30, and CIspi except SPEI90. Moreover, drought classifications identified by M30 are in agreement with that of the observed soil moisture. The results show that M30 based on precipitation and potential evapotranspiration is an appropriate indicator for monitoring drought condition at a finer scale in the study area. According to M30, summer drought during 1970-2014 happened in each year and showed a slightly upward tendency in recent years. [ABSTRACT FROM AUTHOR]

Forecasting is the act of predicting unknown future events using available data. Estimating, in contrast, uses data to simulate an actual condition. Brazil is the world's largest producer of oranges, and the state of São Paulo is the largest producer in Brazil. The 'Valência' orange is among the most common cultivars in the state. We analyzed the influence of monthly meteorological variables during the growth cycle of Valência oranges grafted onto 'Rangpur' lime rootstocks (VACR) for São Paulo, and developed monthly agrometeorological models for forecasting the qualitative attributes of VACR in mature orchard. For fruits per box for all months, the best accuracy was of 0.84 % and the minimum forecast range of 4 months. For the relation between °brix and juice acidity (RATIO) the best accuracy was of 0.69 % and the minimum forecast range of 5 months. Minimum, mean and maximum air temperatures, and relative evapotranspiration were the most important variables in the models. [ABSTRACT FROM AUTHOR]

Soil moisture variability of various spatial scales is analyzed based on empirical orthogonal function (EOF) method using soil moisture datasets with various spatial resolutions: 1 km eco-hydrological model simulation, 0.25° passive microwave (Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E) dataset, and 0.5° land surface model simulation from Climate Predictor Center (CPC). All three datasets generate EOFs that explain similar variances with those generated from in situ observations from agro-meteorological network. Using AMSR-E product and eco-hydrological model simulation, it is found that the primary spatial pattern of soil moisture obtained from watershed scale has a strong connection to topographic attributes, followed by soil texture and rainfall variability, as suggested by the correlation between the primary EOF mode (EOF1) of soil moisture and landscape attributes. However, the EOF analysis of both AMSR-E and CPC datasets at regional scale reaches the conclusion that soil texture indices, such as sand and clay content, is of higher importance to soil moisture EOF1 spatial pattern (explaining 61 % variance) than topography is. Furthermore, correlation between soil moisture EOF1 and soil property is higher in spring than in autumn, which indicates that soil water-holding and drainage capabilities are more important under dry conditions. At national scale, the combined effects of topographic feature and soil property are clearly exhibited in EOF1. The study results reveal that different emphases should be placed on accurate acquisition of landscape attributes for soil moisture estimation according to various spatial scales. [ABSTRACT FROM AUTHOR]

The present study aims at forecasting hard wheat ( Triticum turgidum L. var. durum) yield in northern Greece, a season prior to harvest. It is based on (a) crop simulated, with CERES-Wheat indicators at four planting dates and (b) reported crop yields at two regional levels (three NUTS2 [ Nomenclature of Units for Territorial Statistics] and 16 NUTS3 regions), for the years 1979-2006. Principal component analysis (PCA) was applied to explore major patterns of joint variability in 20 crop simulated agroclimatic indicators of the growing season before harvest. Stepwise regression and hindcast were employed for the selection of the modes identified by PCA as predictors in multivariate linear regression models used for forecasting yield a season ahead of harvest. Forecasting skill varied to a large extent by spatial scale and planting date. When the simulation results aggregated to the larger spatial level (NUTS2), the yield forecasting skill, in terms of R, was rated as high (ranging from 0.48 to 0.73) in three out of four planting dates for Central Macedonia and in one planting ( R = 0.57) for Thrace. Harvest index, nitrogen leaching and related soil water crop simulated output of the previous season, were the most important predictors. No forecasting skill was found in the third NUTS2 region. The performance of the regression models substantially deteriorated at the higher resolution spatial level (NUTS3). In four regions only (including the one where CERES-Wheat was calibrated) yield forecasting skill was moderate ( R > 0.25). The results demonstrate the potential of this approach for regional crop yield forecasting before the beginning of the cropping season. However, crop model calibration is required before its application. [ABSTRACT FROM AUTHOR]

Solar radiation is an important variable for studies related to solar energy applications, meteorology, climatology, hydrology, and agricultural meteorology. However, solar radiation is not routinely measured at meteorological stations; therefore, it is often required to estimate it using other techniques such as retrieving from satellite data or estimating using other geophysical variables. Over the years, many models have been developed to estimate solar radiation from other geophysical variables such as temperature, rainfall, and sunshine duration. The aim of this study was to evaluate six of these models using data measured at four independent worldwide networks. The dataset included 13 stations from Australia, 25 stations from Germany, 12 stations from Saudi Arabia, and 48 stations from the USA. The models require either sunshine duration hours (Ångstrom) or daily range of air temperature (Bristow and Campbell, Donatelli and Bellocchi, Donatelli and Campbell, Hargreaves, and Hargreaves and Samani) as input. According to the statistical parameters, Ångstrom and Bristow and Campbell indicated a better performance than the other models. The bias and root mean square error for the Ångstrom model were less than 0.25 MJ m day and 2.25 MJ m day, respectively, and the correlation coefficient was always greater than 95 %. Statistical analysis using Student's t test indicated that the residuals for Ångstrom, Bristow and Campbell, Hargreaves, and Hargreaves and Samani are not statistically significant at the 5 % level. In other words, the estimated values by these models are statistically consistent with the measured data. Overall, given the simplicity and performance, the Ångstrom model is the best choice for estimating solar radiation when sunshine duration measurements are available; otherwise, Bristow and Campbell can be used to estimate solar radiation using daily range of air temperature. [ABSTRACT FROM AUTHOR]

The climatic potential of maize under dryland farming in Lesotho, southern Africa, was investigated using five suitability indices comprising probability of accumulating heat units of greater than 1,500 growing degree days, probability of a frost-free growing season, probability of seasonal rainfall of more than 500 mm, probability of drought during the flowering to grain-filling stages and the slope of an area. A geographic information system layer was prepared for each of these parameters and the layers overlaid using different weights for each of the climatic suitability indices to obtain an agroclimatic maize suitability map for Lesotho. This analysis yielded different suitability classes. This variability points to prevalence of climatic constraints that need to be acknowledged when attempting to identify management strategies that can optimize the rain-fed maize production in climatically variable environments. [ABSTRACT FROM AUTHOR]

At the present study, factor and cluster analysis were applied to evaluate the agrometeorological suitability zonation of 39 plains in Khorasan Razavi Province, NE Iran. For this purpose, the extracted 98 variables were processed, resulted in eight factors which explain about 90 % of the total variance. The explained variances of these factors are varied from 42.99 to 2.74 % for factors 1 and 8 after the Varimax rotation, respectively. The spatial classification maps of factor scores are extracted after the studied plains and the calculated scores were then classified by cluster analysis. It was revealed that five clusters were enough to classify the studied plains. The 'negligible,' 'low,' 'moderate,' 'high,' and 'very high' classes included 4, 14, 9, 7, and 5 plains with 8.0, 35.6, 23.0, 15.1, and 18.3 % of the total surface area, respectively. The geographical distribution of the five major zones revealed that the plains with very high suitability are laid on the center and north parts of the study area, while the rest which are negligible and low suitability zones are mainly concentrated in the west, south, and southeast of the study area. Comparing the agrometeorological suitability between the corresponding zones revealed a general reducing trend from north to south along 3° geographical latitudes. [ABSTRACT FROM AUTHOR]

Drought is one of the crucial environmental factors affecting crop production. Synchronizing crop phenology with expected or predicted seasonal soil moisture supply is an effective approach to avoid drought impact. To assess the potential for drought avoidance, this study investigated the long-term climate data of four locations (Bojnourd, Mashhad, Sabzevar, and Torbat Heydarieh) in Khorasan province, in the northeast of Iran, with respect to the four dominant crops (common bean, lentil, peanut, and potato). Weekly water deficit defined as the difference between weekly precipitation and weekly potential evapotranspiration was calculated. Whenever the weekly water deficit was larger than the critical water demand of a crop, the probability for drought was determined. Results showed that Sabzevar has the highest average maximum temperature (24.6 °C), minimum temperature (11.7 °C), weekly evapotranspiration (32.1 mm), and weekly water deficit (28.3 mm) and has the lowest average weekly precipitation (3.8 mm). However, the lowest mean maximum temperature (19.7 °C), minimum temperature (6.9 °C), weekly evapotranspiration (22.5 mm), and weekly water deficit (17.5 mm) occur in Bojnourd. This location shows the shortest period of water deficit during the growing season for all crops except potato, which also experienced drought at the end of the growing season. Sabzevar and Torbat Heydarieh experienced the highest probability of occurrence and longest duration of drought during the growing season for all crops. The result of this study will be helpful for farmers in order to reduce drought impact and enable them to match crop phenology with periods during the growing season when water supply is more abundant. [ABSTRACT FROM AUTHOR]

Contrary to the belief that Peninsular Malaysia experiences wet condition throughout the year, prolonged dry condition has lately become a recurrent phenomenon in this region. As a result, country's agricultural sector and water resources have been under severe constraints from this situation. To get a clearer picture of the dry condition in Peninsular Malaysia, the Standardised Precipitation Index, based on the data of monthly rainfall from 50 stations, is derived. Spatial analysis is used to illustrate the percentage of occurrences of dry and very dry events. To evaluate the potential risk due to the dry conditions, we modelled the joint distribution of severity and duration of dry condition by means of bivariate copula. Several copula models were tested, and the model, which best represents the relationship between severity and duration, is determined using Akaike information criterion. Based on the results, the return period for the drought severity, based on the longest duration of drought at each station, can be estimated. This enables the drought risk to be calculated, thus planning on the measures to minimise the impact of a prolonged drought to the societies, which can be done by the relevant authorities. [ABSTRACT FROM AUTHOR]

Sensitivity analysis (SA) is increasingly used to explain models behaviour in response to inputs variation. Agrometeorologists are used to apply standard SA methods only on model parameters because of the difficulty of applying standard sampling techniques to derive series of weather data where each value cannot be sampled independently from those of the neighbouring days and from other variables in the same day. The impact of weather variability on a crop model was here analysed by coupling the Morris SA method to a weather generator. Spring barley in northern Italy was simulated and different outputs considered. Under the explored conditions, parameters involved with temperature generation resulted the most relevant in determining yield and maturity date. Radiation-related parameters were high-ranked for cumulated drainage and actual evapotranspiration. According to the author, this is the first time the sensitivity of a cropping system model to weather variables is quantified using standard SA techniques. [ABSTRACT FROM AUTHOR]

Leaf wetness duration (LWD) is related to plant disease occurrence and is therefore a key parameter in agrometeorology. As LWD is seldom measured at standard weather stations, it must be estimated in order to ensure the effectiveness of warning systems and the scheduling of chemical disease control. Among the models used to estimate LWD, those that use physical principles of dew formation and dew and/or rain evaporation have shown good portability and sufficiently accurate results for operational use. However, the requirement of net radiation (Rn) is a disadvantage foroperational physical models, since this variable is usually not measured over crops or even at standard weather stations. With the objective of proposing a solution for this problem, this study has evaluated the ability of four models to estimate hourly Rn and their impact on LWD estimates using a Penman-Monteith approach. A field experiment was carried out in Elora, Ontario, Canada, with measurements of LWD, Rn and other meteorological variables over mowed turfgrass for a 58 day period during the growing season of 2003. Four models for estimating hourly Rn based on different combinations of incoming solar radiation (Rg), airtemperature (T), relative humidity (RH), cloud cover (CC) and cloud height (CH), were evaluated. Measured and estimated hourly Rn values were applied in a Penman-Monteith model to estimate LWD. Correlating measured and estimated Rn, we observed that all models performed well in terms of estimating hourly Rn. However, when cloud data were used the models overestimated positive Rn and underestimated negative Rn. When only Rg and T were used to estimate hourly Rn, the model underestimated positive Rn and no tendency was observed for negative Rn. The best performance was obtained with Model I, which presented, in general, the smallest mean absolute error (MAE) and the highest C-index. When measured LWD was compared to the Penman-Monteith LWD, calculated with measured and estimated Rn, few differences were observed. Both precision and accuracy were high, with the slopes of the relationships ranging from 0.96 to 1.02 and R2 from 0.85 to 0.92, resulting in C-indices between 0.87 and 0.93. The LWD mean absolute errors associated with Rn estimates were between 1.0 and 1.5 h, which is sufficient for use in plant disease management schemes. [ABSTRACT FROM AUTHOR]