Assessment of the suitability of temperature and relative humidity for rice cultivation in rainfed lowland paddy fields in Kurunegala district

Rathnayake, W. M. U. K.; Silva, R. P. De; Dayawansa, N. D. K.

doi:10.4038/tar.v27i4.8214

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…Terminal and continual heat stresses are two major constraints influencing crops growth and development. The temperature threshold levels were reported at different stages for crops viz., cotton [ 9 , 10 , 11 , 12 , 13 ], rice [ 14 , 15 , 16 , 17 , 18 , 19 ], sorghum [ 20 , 21 ], barley [ 12 ], maize [ 9 , 22 , 23 ], and soybean [ 23 ]. Wheat is an imperative staple food, the cheapest energy source, provides 8–20% of protein, and 70–75% of calories in our average diet [ 24 ], but a high temperature restricts the wheat crop to express its full genetic potential.…”

Section: Introductionmentioning

confidence: 99%

Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies

Khan

Ahmad

Ahmed

et al. 2020

Plants

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Temperature across the globe is increasing continuously at the rate of 0.15–0.17 °C per decade since the industrial revolution. It is influencing agricultural crop productivity. Therefore, thermotolerance strategies are needed to have sustainability in crop yield under higher temperature. However, improving thermotolerance in the crop is a challenging task for crop scientists. Therefore, this review work was conducted with the aim of providing information on the wheat response in three research areas, i.e., physiology, breeding, and advances in genetics, which could assist the researchers in improving thermotolerance. The optimum temperature for wheat growth at the heading, anthesis, and grain filling duration is 16 ± 2.3 °C, 23 ± 1.75 °C, and 26 ± 1.53 °C, respectively. The high temperature adversely influences the crop phenology, growth, and development. The pre-anthesis high temperature retards the pollen viability, seed formation, and embryo development. The post-anthesis high temperature declines the starch granules accumulation, stem reserve carbohydrates, and translocation of photosynthates into grains. A high temperature above 40 °C inhibits the photosynthesis by damaging the photosystem-II, electron transport chain, and photosystem-I. Our review work highlighted that genotypes which can maintain a higher accumulation of proline, glycine betaine, expression of heat shock proteins, stay green and antioxidant enzymes activity viz., catalase, peroxidase, super oxide dismutase, and glutathione reductase can tolerate high temperature efficiently through sustaining cellular physiology. Similarly, the pre-anthesis acclimation with heat treatment, inorganic fertilizer such as nitrogen, potassium nitrate and potassium chloride, mulches with rice husk, early sowing, presoaking of a 6.6 mM solution of thiourea, foliar application of 50 ppm dithiothreitol, 10 mg per kg of silicon at heading and zinc ameliorate the crop against the high temperature. Finally, it has been suggested that modern genomics and omics techniques should be used to develop thermotolerance in wheat.

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Section: Introductionmentioning

confidence: 99%

Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies

Khan

Ahmad

Ahmed

et al. 2020

Plants

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“…For example, temperature less than 19°C is critical for inducing grain sterility in paddy [27] but the temperature in the equatorial paddy growing areas does not usually drop down that much. Similarly, the optimum relative humidity for paddy cultivation lies between 60% and 80%, while values higher than 85% are critical [28]. However, the spikelet fertility was not always inhibited only by high relative humidity [29].…”

Section: Discussionmentioning

confidence: 99%

“…Rather, it induces almost complete paddy sterility at a temperature of about 35°C [27]. Hence, higher temperatures with high relative humidity decrease paddy yield [30] proving that the combined effect of temperature and relative humidity is a predominant factor in paddy cultivation [28]. In this sense, a comprehensive analysis in the area of interest is required to understand the relationship between weather and paddy yield there.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

Ekanayake

Rankothge

Weliwatta

et al. 2021

Journal of Mathematics

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This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.

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“…Higher relative humidity has a negative impact during ripening phase. Rathnayake et al (2016) reveal that higher relative humidity and higher temperature during flowering stage has a negative impact.…”

Section: Weather and Non-weather Variables Affecting Yieldmentioning

confidence: 96%

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

Chandra

Mitra

Dubey³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The development of kharif rice yield prediction models was attempted through Machine Learning approaches such as Artificial Neural Network and Random Forest for the 42 blocks covering 13,141 sq km upland rainfed area of Purulia and Bankura district, West Bengal. Models were developed integrating monthly NDVI with weather and non-weather variables at block-level for the period 2006 to 2015. The model correlation obtained was 0.702 with MSE 0.01. Though the weather variables vs NDVI models are quite satisfactory, NDVI vs kharif rice yield models however, show relatively less correlation, about 0.6 revealing the requirement of varied additional farmer-controlled inputs. Development of NDVI vs crop yield models for different crop growth stages or fortnightly over a larger data set with selective adding of weather and non-weather variables to NDVI would be the most appropriate.This paper focus on developing kharif rice yield prediction models for rainfed upland area through machine learning techniques integrating block (sub-district) level NDVI with weather and non-weather variables.

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Assessment of the suitability of temperature and relative humidity for rice cultivation in rainfed lowland paddy fields in Kurunegala district

Cited by 13 publications

References 11 publications

Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies

Rising Atmospheric Temperature Impact on Wheat and Thermotolerance Strategies

Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka

Machine Learning Approach for Kharif Rice Yield Prediction Integrating Multi-Temporal Vegetation Indices and Weather and Non-Weather Variables

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