The decrease in precipitation fraction due to moderately wet days was particularly evident in the eastern and western parts of the valley. Empirical assessment of the projected impact of climate change scenarios on tea yield is likely to be positive in the next three decades.
Impact of Climate Change on Rice Productivity: An Empirical and Simulation Modeling Approach
Impact of Climate Change on Tea Productivity: An Empirical Statistical Approach
List of Tables
Introduction
- Background
- Objectives
- Organization of the thesis
The past impact of climate change and variability on recent changes in rice and tea productivity needed to be empirically examined. The outcome of the study on the impact of climate change in the Brahmaputra valley and the resulting impact on rice and tea production is divided into seven chapters.
Review of Literature
- Climate Change and Climate Variability
- Changes in temperature
- Changes in rainfall
- Trends of extreme weather events
- Extreme temperature events
- Extreme rainfall events
- Changes in solar radiation
- Climate change projection
- Impact of climate change on agriculture .1 General facts
- CO 2 fertilization effect
- Effect of temperature and precipitation
- Effect of climate variability and extreme weather events
- Impact on pest and diseases
- Methods for assessing climate change impact on Agriculture
- Impact of climate change on growth and yield of rice
- Impact of climate change on growth and yield of tea
- Adaptation-mitigation strategies for rice and tea crop
- Summary
The recent work in Hansen et al. 2012) describe that future (warmer) climates will experience the emergence of a new category of extreme outliers (>3σ above the mean). They have been addressed to a much lesser extent in the assessment of climate change effects (Adams et al. 1998).
Study Area
- Location
- Physiography
- Climatic features
- Variations of seasonal weather
- Pre-monsoon season
- Monsoon season
- Post-monsoon season
- Winter season
- Spatial distribution of rainfall
- Spatial distribution of temperature
- Spatial distribution of sunshine hour
- Present Status of Agriculture
- Cropping Pattern
- Soils
- Natural hazards
In the Brahmaputra valley, the alluvial deposits of the river Brahmaputra during recent geological times mainly form the soils. Barua (1989) classified the soils of the Brahmaputra valley into three groups viz. i) the soils which owe their origin mainly to the sediments of the Brahmaputra River which are usually sandy with poor nutrients. The tea soil of the Brahmaputra valley is mostly alluvial (uplands) and varies greatly in texture.
Recent estimates show that the extent of occurrence of acidic soil in the Brahmaputra valley is more than 90% (Talukdar et al. 2004).
Trends and Fluctuations of Rainfall, Temperature and Sunshine
Duration
Materials .1 Climate data
- Data quality and homogeneity
Outliers were abandoned or accepted with confirmation from the comparison plot of neighboring stations. Some deviations in maximum and minimum daily temperature exceeding ±4 standard deviations were also found and replaced by the normal value of the respective station. Homogeneity of seasonal precipitation and temperature series was tested using Bartlett's short test (Mitchell et al. 1966).
Let S2max and S2min denote the maximum and minimum values of Sk2.
Methods
- Estimation of trends
- Magnitude of trends
Here, the entire rainfall series was divided into 5 subperiods (k) with 22 observations (n) in each subperiod. All rainfall and temperature series considered in this study were found to be homogeneous (Table A.1 and A2). If there is only one datum in each time period, then N = n(n–1)/2, where n corresponds to the number of time periods.
The N slope values are ordered from smallest to largest and if N is odd, the Sen slope estimator is calculated as .
NmedianQ
Significance of trends
The S statistic, in cases where the sample size n is greater than 10, is assumed to be asymptotically normal, with E(S) = 0 and the variance of S is calculated by. Where t refers to the extent of a given bond and Σt indicates the summation over all bonds. A tie is the sample data with the same value and the summation is over all ties.
In a two-tailed test for trend, the null hypothesis H0 should be accepted if [Z] > Za/2, at the significance level.
Short-term fluctuations
Where R is the average and σ the standard deviation of the set for the total number of investigated years N; Rk is the average of consecutive n-years.
Excess and deficient rainfall years
Extreme rainfall and temperature indices
In addition to station-level analysis, trend analysis was performed for three sub-regions (eastern, central and western) as well as for the Brahmaputra valley as a whole. Similarly, the extreme indices for the Brahmaputra valley as a whole were estimated by averaging the indices of all three individual parts. Since the extreme temperature indices showed broad spatial correlation (Alexander et al. 2006), the analysis was performed for the Brahmaputra valley as a whole.
The extreme temperature indices for the Brahmaputra valley were also calculated by arithmetic averages of the indices of all the four stations considered in the study.
Results and Discussion .1 Rainfall analysis
- Descriptive Statistics
- Monthly rainfall trends
- Annual rainfall trends
- Seasonal rainfall trends .1 Pre-monsoon
- Short-term fluctuations of rainfall
- Shift in decade-wise annual rainfall
- Shift in decade-wise seasonal rainfall
- Trends of extreme rainfall indices
- Threshold based indices
- Absolute index (Rx1-day)
- Simple Daily Intensity Index (SDII)
- Percentile-based indices
- Temperature analysis .1 Descriptive statistics
- Monthly temperature trends
- Seasonal and annual temperature trends .1 Maximum temperature
- Trends of extreme temperature indices
- Intensity Indices
- Frequency indices with percentile thresholds
- Frequency indices with fixed thresholds
- Range index (DTR)
- Changes in annual cycles of extreme temperature events
- Trend of sunshine duration
- Monthly trends
Pre-monsoon rainfall in the Brahmaputra Valley has shown a small increasing trend over the last 110 years (Table 4.8 and Figure 4.7). During NP1, pre-monsoon rainfall increased by 38.0 mm/decade in the Brahmaputra Valley, mainly due to the increase in the. Pre-monsoon rainfall showed an increasing trend (17.4 mm/decade) over the Brahmaputra Valley during NP3 due to a statistically significant increase in April rainfall in the western part (Table 4.7).
During the NP, post-monsoon rainfall in the Brahmaputra Valley increased at a rate of 16.1 mm/decade due to increasing October rainfall in all three parts of the valley (Table 4.7). The magnitude of the increase was highest in the western part (17.6 mm/decade) of the valley. However, in the recent thirty year period, SDII has increased in all parts of the Brahmaputra Valley during pre-monsoon and post-monsoon and decreased during monsoon season (Table 4.10).
Impact of Climate Change on Rice Productivity: An Empirical
Statistical and Simulation Modeling Approach
Introduction
Study site
Accurate understanding of the relationship between site-specific climate variability and rice yield is critical to maintaining productivity. However, during the rice growing season (June/July to November/December), the average seasonal maximum and minimum temperatures are 29.5 °C and 21.4 °C respectively. Jorhat is normally a rice growing area where 57% of the total geographical area is covered by rice (Vadivelu et al. 2004b).
Materials and Methods
- Observed impact of climate on rice productivity .1 Yield data
- Climate data
- Development of statistical model
- Projected impact of climate change on rice productivity
- Details of field experiments
- Evaluation of CERES-Rice model
- Design of experiment for evaluating climate change impact on rice productivity
This variety of rice is one of the popularly grown varieties in the Brahmaputra Valley during the sali (kharif) season. Calculation of the genetic coefficient of cultivars is the first step in the conventional use of the CERES models. A detailed description of the cultivar coefficients used by the CERES-Rice model is shown in Table 5.3.
The sensitivity of the CERES-Rice model to temperature, CO2 levels, rainfall and solar radiation was analyzed by randomly creating anomalies in the created base climate data.
Results and Discussion .1 Descriptive statistics
- Observed impact of climate change on rice productivity
- Future impact of climate change on rice productivity
- Calibration of CERES-Rice model
- Validation of CERES-Rice model
- Growth and yield of rice under baseline scenario
- Sensitivity Analysis of CERES-Rice Model
- Yield of rice under different climate change scenarios
Average air temperature during planting to harvest period of rice under different planting dates in the study area was 28°C or less (Table 5.11) which may not be sufficient for the manifestation of the fertilizing effects of CO2. Predicted changes (%) in rice yield with respect to the control under different climate change scenarios and planting dates are presented in Table 5.17. Average relative yield increase of rice was positive under all the climate change scenarios considered (Table 5.17), regardless of planting dates.
Among the different transplanting dates, the relative yield increase was the highest (over 10%) in crops transplanted on August 9 (Table 5.17) in these two climate change scenarios (scenarios E and F).
Impact of Climate Change on Tea Productivity: An Empirical
Statistical Approach
Introduction
Assam is the largest producer of tea, contributing 53.2% of the total area and 51.43% of the total production in the country (Tea Statistics Tea Board of India 2007a, 2007b). About 88% of Assam's tea area is concentrated in the Brahmaputra Valley alone (Figure 6.2) with a production of 390 million kg (90% of the country's total tea production). The area, production and average yield of tea in the Brahmaputra Valley during 1961–2007 are shown in the figure.
Fig.6.3 Area, production and yield of tea in the Brahmaputra valley during Source: Tea Statistics Annual Report Tea Board of India (various editions)].
Study site
- Climatic characteristics
- Soils characteristics
The climate of the study area is sub-tropical humid with an average annual maximum and minimum temperature of 28.2°C and 19.1°C respectively. The soil of the study area is characterized as fine loamy, mixed, hyperthermic family of Typical Distrudepts. The location of the garden is characterized by alluvium-derived, well-drained, very gently sloping uplands (Vadivelu et al. 2004a).
The organic matter content of the surface horizon is 1.38% and its value decreases with depth (Table 6.1).
Materials and methods .1 Yield data
- Climate data
- Statistical analysis
- Model for estimating observed impact of climate on productivity
- Model for estimating projected impact of climate on productivity
- Building up of climate change scenarios
Therefore, temperature and sunshine duration data were also calculated by averaging TES and AAU data and are assumed to be representative of the entire estate. Different climate variables were grouped into four seasons according to the climatic water balance of the study area (Section 6.2.1). Being a perennial crop, the yield of tea is affected by the weather conditions of the preceding months or seasons in addition to the weather of the picking season (Sen et al. 1966).
The variability was measured by the coefficient of variation (CV) calculated as the seasonal ratio of the standard deviation to the mean of each climate variable (Rowhani et al. 2011).
Results and Discussion .1 Descriptive Statistics
- Impact on early crop
- Impact on late crop
- Projected impact of climate change on tea productivity
- Impact on early crop
The estimated impact of individual climatic variables on late crop yield trend is presented in Table 6.10. The decrease in the variability of the number of rainy days (CvRD4) during the post-monsoon season caused the yield to increase by Table 6.10. The predictive model developed by regressing actual early crop yield and climate parameters is shown in Table 6.11 and Fig 6.9.
The yield prediction model developed for the main tea crop is shown in Table 6.12 and Fig.
Early crop
Rainfall and variability of its distribution during pre-monsoon (R2 and CvRD2), total rainfall and rainy days during monsoon (R3 and RD3) and sunshine duration during winter, pre-monsoon and monsoon season (SSH1, SSH2 and SSH3) are important factors for predicting the future yield of tea during the main season (June-September). If the interseasonal variability of rainy days during the pre-monsoon season (CvRD2) increases by 10%, the yield increases by 1%. When only the number of rainy days during the monsoon was reduced by 20% over the baseline, the relative yield increased by 5%.
This result indicated that reducing the number of rainy days without reducing the amount of rainfall during the monsoon season is beneficial for the yield of the main crop.
Main crop
Air temperature during the post-monsoon season was found to be positively correlated with late crop yield (r= 0.335 for maximum temperature and r= 0.321 for minimum temperature). Irrigation in November/December combined with fertilization in late September with available soil moisture from monsoon rains will invariably increase late crop yields. These two parameters during the post-monsoon season are also significant in determining the late crop yield.
But this positive effect of warmer winter on early crop yield or warmer post-monsoon on late crop yield will be largely determined by the amount of rainfall received during these two seasons.
Late crop
Thus, the low response to day length when nights are cool suggests that the yield decline during winter months is due to cold night temperatures rather than day length. Likewise, light irrigation in mid-November is likely to increase late-crop yields. In addition, mulching, an important technique for reducing water depletion of the profile, can be applied, especially in the small tea farms.
The main conclusion from this study is the impact of climate change and its variation on tea yield under the prevailing agro-climate of the upper Brahmaputra valley is likely to be positive in the near future.
