^{1}

^{2}

^{1}

^{2}

^{1}

^{1}

^{2}

Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (

Rice is the staple food of almost all Sri Lankans. Therefore, it is estimated that 2.7 million metric tons of rough rice (paddy) is produced annually to satisfy the demand (around 95%) of the country [

With increasing global temperatures, resulting deviations in rainfall patterns cause immense impact on the crop growth. Thus, the water availability for crops essentially depends upon rainfall distribution. Moreover, intense and excess rainfall can produce adverse effects, along with major flooding devastating vegetation, while crop yield also reduces due to water shortage in drought climates. Nevertheless, rice cultivation is considered a semiaquatic plant grown at a controlled supply of water. The source of water supply and degree of flooding are treated to be some environmental factors which determine the paddy harvest.

Paddy cultivation in Sri Lanka takes place under different geographical and hydrological conditions with different soils and elevations. Cascade-type (

Therefore, many researchers investigated the relationships between the various climatic factors to not only paddy but also to various other crops [

Weather and climatic factors, such as rainfall, temperature, humidity, and sunshine hours, and soil factors, such as pH, texture, and organic matter content of soil are few of the many factors affecting the crop production [

However, according to the authors’ knowledge, no research has been carried out in determining the paddy harvest (yield) with respect to the various climatic factors in the context of Sri Lanka. Therefore, the objective of this paper is to understand the relationships among climatic factors and rice production in Sri Lanka. Three algorithms were used and compared in the development of the training process of ANN which predicts the paddy yield with respect to the various climatic factors. The authors of this paper believe that this would be the first study in the context of Sri Lanka to incorporate the ANN to paddy yield.

As it was stated earlier, usage of ANN was frequent in many nonlinear real-world problems. ANN required three layers in minimum for its development; input layer, hidden layer, and output layer (refer Figure

Structure of neural network.

The literature shows many algorithms are used in ANN to optimize the training process. Among them, Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG) are three frequently used algorithms in ANN [

Gradient descent method and Gauss–Newton method are combined in this algorithm. When the Gauss–Newton method is used to express the backpropagation of neural network, the algorithm has a higher probability to reach an optimal solution [

Similar to LM algorithm, Bayesian Regularization (BR) algorithm updates the learning algorithm’s weights and bias values and minimizes the linear combination of squared errors and weights. In addition, BR algorithm modifies the linear combination, and as a result, the network has good generalization qualities by the end of the training. LM and BR algorithms are considered to have the ability to obtain lower mean squared errors compared to other algorithms for functioning approximation problems [

Scaled Conjugate Gradient (SCG) is considered the most popular iteration algorithm used in solving problems of large systems of linear equations [

MATLAB numerical computing environment (version 8.5.0.197613-R2015a) was used to develop the ANN architectures to predict the paddy yield. One hidden layer was included in the ANN architecture with the dependent variable of the paddy yield. The climatic parameters were included as the input parameters of the ANN model.

Time series data for each input and output parameter were divided into three clusters: for training (70%), for validation (15%), and for testing (15%) of all data. The training step was started with selection of the training algorithms. The above stated three training algorithms, namely; Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient were used. The performance of each training algorithm was evaluated based on the value of mean squared error (

Coefficient of correlation and

Sri Lanka is located in the Indian Ocean and lies between the latitudes of 5°55 N and 9°51 N and the longitudes of 79°41 E and 81°53 E. It covers a land area of 65,610 km^{2}. Sri Lanka is divided into nine provinces and twenty-five districts for administrative purposes. Eight districts were chosen for this analysis based on the data availability. More importantly, these eight districts are the major paddy grown districts in Sri Lanka. These districts are, namely, Ampara, Batticaloa, Badulla, Hambantota, Kurunegala, Puttalam, Trincomalee, and Vavuniya (refer Figure

Location map of the study area.

Sri Lanka has tropical climate conditions. The average annual temperature varies from 28°C to 30°C and annual average diurnal temperature variability ranges from 4°C to 7°C. Sri Lanka is under two major monsoon winds, and they bring significant amount of rainfall to the whole country. The two major monsoons are southwest monsoon (May to September) and northeast monsoon (December to February). In addition to these, there are two intermonsoons; 1st intermonsoon (March and April) and 2nd intermonsoon (October and November). Therefore, the country is rich in its receiving rainfall. However, not only the temporal variations but also the spatial variations affect the receiving rainfall. Therefore, the annual rainfall ranges from 600 mm in the arid areas to 6,000 mm in the very wet areas [

These rainfall patterns were used in developing two agricultural seasons in the country: Maha season and Yala season. The major agricultural season is the Maha season, and it spans from the September to March of the following year, whereas the Yala season spans from May to August. The Maha season dominates with rain-fed agriculture; however, irrigated water from tanks dominates in the Yala season for the water requirement of the paddy fields.

Rainfall (mm), morning and evening relative humidity (%), minimum and maximum temperature (°C), wind speed (km/hr), evaporation (mm), and sunshine hours (hr) are used as the climatic factors to predict the paddy yield. However, several climatic combinations for different time spans were used for each district. This is because of the nonavailability of some of the data. Table

Summary of the available climatic data.

District | Gauging station | Available climatic data | Time span |
---|---|---|---|

Ampara | Potuvil | Rainfall (RF), morning and evening relative humidity (RH), minimum and maximum temperature (_{min} and _{max}) | 2009–2015 |

Batticaloa | Batticaloa | 2009–2015 | |

Hambantota | Hambantota | 1987–2015 | |

Trincomalee | Trincomalee | 1995–2015 | |

Badulla | Badulla | 2003–2015 | |

Bandarawela | 1994–2015 | ||

Vavuniya | Vavuniya | 2009–2015 | |

Kurunegala | Kurunegala | Rainfall (RF), wind speed (WS), minimum and maximum temperature (_{min} and _{max}), evaporation (EV), sunshine hours (SH) | 2004–2018 |

Puttalam | Puttalam | 2000–2017 |

The monthly climatic data were obtained from the Department of Meteorology and Department of Census and Statistics, Sri Lanka. The corresponding paddy yield data for two seasons (

Depending on the data availability, neural networks were run to various climate combinations to obtain the relationships given in equation (

Equation (

Figures

Coefficient of correlations for combined Maha and Yala seasons under LM, BR, and SCG algorithms for Badulla. (a) For training: LM algorithm. (b) For validation: LM algorithm. (c) For test: LM algorithm. (d) For all: LM algorithm. (e) Validation performance: LM algorithm. (f) For training: BR algorithm. (g) For validation: BR algorithm. (h) For training: SCG algorithm. (i) For validation: SCG algorithm.

In addition, computational efficiency in the process is shown in Figure

Furthermore, Figures

Figure

Coefficient of correlations for Maha and Yala seasons under LM algorithm for Badulla and Kurunegala districts. (a) For training–: Badulla Maha season under LM algorithm. (b) For validation: Badulla Maha season under LM algorithm. (c) For training: Badulla Yala season under LM algorithm. (d) For validation: Badulla Yala season under LM algorithm. (e) For training: Kurunegala Maha season under LM algorithm. (f) For validation: Kurunegala Maha season under LM algorithm. (g) For test: Kurunegala Maha season under LM algorithm. (h) For all: Kurunegala Maha season under LM algorithm.

It would be interesting to investigate the results from the relationship given in equation (

Table

Correlation coefficients for different algorithms for the combined Maha and Yala seasons data.

Area | ANN algorithm | Correlation coefficient | |||
---|---|---|---|---|---|

Training | Validation | Testing | All | ||

Potuvil | LM | 1.00 | 1.00 | 1.00 | 0.96 |

BR | 0.97 | 1.00 | 0.95 | 0.92 | |

SCG | 0.92 | 1.00 | 1.00 | 0.92 | |

Batticaloa | LM | 0.81 | 1.00 | 1.00 | 0.86 |

BR | 0.90 | 1.00 | 0.69 | 0.86 | |

SCG | 0.93 | 1.00 | 1.00 | 0.79 | |

Hambantota | LM | 0.91 | 0.77 | 0.50 | 0.82 |

BR | 0.71 | 0.64 | 0.70 | 0.78 | |

SCG | 0.82 | 0.89 | 0.64 | 0.75 | |

Trincomalee | LM | 0.99 | 0.59 | 0.79 | 0.87 |

BR | 0.66 | 0.82 | 0.68 | 0.75 | |

SCG | 0.80 | 0.87 | 0.78 | 0.75 | |

Badulla | LM | 0.85 | 0.95 | 0.81 | 0.85 |

BR | 0.58 | 0.64 | 0.60 | 0.85 | |

SCG | 0.78 | 0.99 | 0.86 | 0.76 | |

Bandarawela | LM | 0.88 | 0.75 | 0.86 | 0.78 |

BR | 0.81 | 0.88 | 0.75 | 0.69 | |

SCG | 0.67 | 0.63 | 0.91 | 0.69 | |

Vavuniya | LM | 1.00 | 1.00 | 1.00 | 0.92 |

BR | 0.98 | 1.00 | 0.71 | 0.92 | |

SCG | 0.86 | 1.00 | 1.00 | 0.84 | |

Kurunegala | LM | 0.98 | 0.80 | 0.54 | 0.85 |

BR | 0.73 | 0.74 | 0.74 | 0.81 | |

SCG | 0.85 | 0.88 | 0.43 | 0.80 | |

Puttalam | LM | 0.94 | 0.96 | 0.75 | 0.92 |

BR | 0.69 | 0.59 | 0.62 | 0.56 | |

SCG | 0.70 | 0.78 | 0.47 | 0.65 |

Table

Validation performance for the combined Maha and Yala seasons’ data.

Area | ANN algorithm | MSE | Number of epochs |
---|---|---|---|

Potuvil | LM | 0.193 | 5 |

BR | 0.021 | 230 | |

SCG | 0.074 | 12 | |

Batticaloa | LM | 0.040 | 5 |

BR | 0.093 | 203 | |

SCG | 0.084 | 11 | |

Hambantota | LM | 0.155 | 10 |

BR | 0.146 | 719 | |

SCG | 0.141 | 25 | |

Trincomalee | LM | 0.334 | 8 |

BR | 0.197 | 552 | |

SCG | 0.205 | 15 | |

Badulla | LM | 0.055 | 6 |

BR | 0.386 | 31 | |

SCG | 0.196 | 13 | |

Bandarawela | LM | 0.204 | 8 |

BR | 0.129 | 554 | |

SCG | 0.308 | 10 | |

Vavuniya | LM | 0.019 | 4 |

BR | 0.006 | 212 | |

SCG | 0.131 | 12 |

The results clearly show that paddy production has a strong correlation with climatic factors. However, in the absence of some of the climatic data, the combination of two cultivation seasons has produced improved results. Therefore, these results are stable.

As it was stated in Section ^{2}

However, Das et al. [

Nonlinear complex relationships among various climatic factors and paddy yield were obtained for several districts in Sri Lanka using artificial neural networks. Comparative analysis using three different training algorithms shows that the LM training algorithm is better than other two BR and SCG algorithms. However, BR and SCG algorithms have also produced acceptable results. Therefore, LM algorithm can be used in future predictions at lower computational costs. The correlation coefficients from the tests were not the best for predictions as they are numerically around 0.65–0.8. However, as the literature suggests these correlation coefficients are acceptable in the context of highly varying climate scenarios. Therefore, it can be concluded herein that there is an acceptable correlation in between the paddy yield and the climatic factors. Thus, it can be concluded that the paddy yield is significantly impacted due to on-going climate change.

More importantly, these nonlinear relationships are available with the analysis. Therefore, reverse calculations are possible in the presence of future climate data. Various climate models can be used to extract future climate data, and therefore, the ANN models can be rerun to obtain the corresponding paddy yield data. Therefore, this prediction would be highly important to the planners and authorities to arrange sustainable cultivation patterns in the future. However, as discussed, the predictions are obtained with an error threshold due to unpredicted climate variables, spatial nonuniformity of climatic data, and the quality of the recorded data. Nevertheless, the planners and farmers are not expecting perfect predictions, but acceptable predictions.

In addition, in extreme climate events, the models can be used to prepare the proper schedules for food sustainability. Nevertheless, the models have to be further improved using more climatic parameters. In addition, it would be interesting to investigate the relationships among other important variables such as technological improvements in agriculture and new crop varieties, which can withstand some climate extremes and various negative impacts from various pests in paddy cultivation.

The data used to support the findings of this study are available from the corresponding author upon request.

The research was carried out in the Sri Lanka Institute of Information Technology and Wayamba University of Sri Lanka environments.

The authors declare that they have no conflicts of interest.

Authors are grateful to Sri Lanka Institute of Information Technology, Sri Lanka for providing financial support to carry out this research. In addition, the authors would like acknowledge the support that they have received from Department of Census and Statistics, Sri Lanka and Department of Irrigation, Sri Lanka to conduct this research work. Furthermore, the contribution received from Wayamba University of Sri Lanka is notable.