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Use of seasonal climate information to predict coconut production in Sri Lanka
International Journal of Climatology
Abstract
Accurate forecasting of annual national coconut production (ANCP) is important for national agricultural planning and negotiating forward contracts. Climate and the long-term trends (attributed to ‘technology’) are major factors that determine ANCP. The effect of climate on ANCP of the following year was studied for the seven agro-ecological regions (AER's) in the principal coconut growing areas for the period 19502002. Climate was studied based on seasons aggregated by the monsoon calendar and by quarters that are consistent with the agricultural calendar. The use of quarterly seasons explained more of the variability of ANCP than the use of monsoon based seasons. January–March rainfall in all AER's and July–September rainfall in the wetter regions are positively correlated with the ANCP (p < 0.005). The technology effect was estimated using a log–linear trend model. The regression model integrates both climate and technology effects developed to predict ANCP with high fidelity (R2 = 0.94). The climate effect was estimated by regressing production data that had been de-trended to remove the technology effects with quarterly rainfall in the year prior to harvest. The most significant predictors were found to be the quarterly rainfall from the AER's in the coconut growing regions that are designated as wet and intermediate. Representative rainfall from each quarter was used in a regression model with corrections for the technology effect. The correlation between observed and predicted values of the ANCP was 0.83 (p < 0.001). The prediction of ANCP for 2003 and 2004 gave errors of only 6.5 and 7.0%. The estimated value of ANCP for 2005 is 2715 million nuts, which is 12% higher than the mean. The lead time of the prediction extends to 15 months but it may be extended with the use of seasonal climate forecasts to 24 months. Copyright © 2007 Royal Meteorological Society
... Rainfall during November and December had significant adverse effect on coconut in Sri Lanka. Peiris et al. (2008) using multiple linear regression reported positive effect of rainfall during January-March in all agro-ecological regions (AERs) and July-September on coconut production in the wetter regions of Sri Lanka. Rainfall, relative humidity and temperature of previous year during February, June, July, September and December months influenced the coconut yield to a large extent (Peiris and Thattil 1998). ...
... But the main problem of this study was that the predictions were qualitative in terms of high, medium and low. Though prediction of coconut yield by means of simple and advance regression models based on weather parameters has been studied previously (Peiris et al. 2008;Naresh Kumar et al. 2009b;Jayashree et al. 2015;Jayakumar et al. 2016), comparison of multiple statistical models received much lesser attention. On the other hand, multiple linear regression (MLR) technique can be accepted for a smaller dataset but its application is restricted when the number of predictors is greater than the number of samples (Balabin et al. 2011). ...
... On the other hand, PCA does not consider the dependent variable during transformation of input variables. Previous studies on coconut yield prediction mainly used simple linear regression models with specific monthly or seasonal climatological data (Peiris et al. 2008;Naresh Kumar et al. 2009b) ignoring the contribution of remaining months or seasons data. As coconut is a perennial crop, use of year-round monthly or seasonal data is better than using only specific monthly or seasonal data. ...
Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000–2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model’s performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45–0.99 and 18–3624 nuts ha−1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.
... Growth cycle of a coconut bunch lasts for 38 months, from the initiation of the sinflorescence primordium to full maturity of the nuts (Peiris et al., 2008). Of the total period, pre-fertilization phase lasts for 27 months in which the inflorescence is covered by a spathe (Fig. 1). ...
... Coconut yield depends on climatic variables such as rainfall, temperature and relative humidity in addition to other external factors such as pest attacks, diseases, crop management, land suitability and nutrient availability (Peiris et al., 2008). Optimum weather conditions for the growth of coconut include a well distributed annual rainfall of about 1500 mm, a mean air temperature of 27°C and relative humidity of about 80-90% (Peiris et al., 1995). ...
... A study carried out by Peiris et al. (2008) shows how seasonal climate information is used to predict coconut production in Sri Lanka. Rainfall is identified as the principal element that influences the yield variability across different agro-ecological regions. ...
... During this second season only about one-half of the total agricultural land is cultivated because of a limited supply of water. Rainfall variations during both seasons have been shown to influence rice and coconut production (Zubair 2002;Peiris et al. 2008). ...
... Annual rainfall is greatest in the southwest of the country (Fig. 1), with a relatively drier climate in other locations (Zubair et al. 2008). With an average annual rainfall of roughly 1800 mm Sri Lanka may appear unlikely to be significantly impacted by drought. ...
... Relationships between Sri Lankan rainfall and sea surface temperatures (SST) in the Pacific Ocean have been described by Rasmusson and Carpenter (1983), Suppiah (1996), and Zubair et al. (2008). These studies (and several others) point to a strong relationship with ENSO that varies seasonally. ...
... This study emphasized the potential benefits that can be gained through adaptation strategies. Coconut production forecasting studies have shown that annual coconut production is particularly sensitive to rainfall during January to March in the main coconut growing regions (Peiris et al., 2008). Further, maximum ambient temperature and relative humidity in the afternoon are the most significant variables in nut production (Peiris & Thattil, 1997). ...
... Coconut production is closely tied to the distribution pattern of rainfall in previous years ( Figure 7). Therefore, considering rainfall as the most important yield determining factor, lagged rainfall is often used in coconut yield prediction studies (Peiris et al., 2008;Peiris et al., 2000). A model including lagged quarterly rainfall of the previous year was capable of predicting yield 15 months ahead (Peiris et al., 2008). ...
... Therefore, considering rainfall as the most important yield determining factor, lagged rainfall is often used in coconut yield prediction studies (Peiris et al., 2008;Peiris et al., 2000). A model including lagged quarterly rainfall of the previous year was capable of predicting yield 15 months ahead (Peiris et al., 2008). Prolonged droughts affect coconut production and the impact lasts for nearly four years due to the 44 month development cycle of an inflorescence. ...
The coconut industry is an important source of foreign exchange and employment generation for Sri Lanka, and an essential component of Sri Lankan cuisine, nutrition and rural livelihood. This paper describes the current status of the industry reviewing its behaviour over the recent past and assessing future possibilities. Coconut occupies 20 percent of Sri Lankan arable land and the majority (82 percent) is operated at small scale. Nearly 63 percent of production is domestically consumed and this proportion is linked with the increasing population. Average coconut yields have been stagnant over time but there is substantial year to year variability due to climatic factors. This volatility generates intense competition for raw materials among the various processing industries. The government allows substitute edible oil imports and bans fresh nut
Exports when fresh nut prices are increasing. Future coconut supply is uncertain due to climate change and its unknown impacts. However, effective adaptation measures may limit the expected vulnerability depending on the severity of change. The uncertainties of future coconut supply may affect domestic consumers, producers and the coconut processing industries. An analysis of adaptation strategies to predicted climate change for the Sri Lankan coconut industry value chain is considered to be an important research issue.
... This study emphasized the potential benefits that can be gained through adaptation strategies. Coconut production forecasting studies have shown that annual coconut production is particularly sensitive to rainfall during January to March in the main coconut growing regions (Peiris et al., 2008). Further, maximum ambient temperature and relative humidity in the afternoon are the most significant variables in nut production (Peiris et al., 1997). ...
... Previous yield estimation and prediction studies for coconut in Sri Lanka which attempted to develop statistical relationships between annual yield and climatic factors were not conclusive due to the complex nature of coconut yield (Abeywardena, 1966(Abeywardena, , 1968Brintha et al., 2012;Peiris et al., 2008;Peiris et al., 2000;Peiris et al., 1997;Peiris et al., 1995;Peiris, 1998;Peiris, 1991Peiris, -1993. Process based models such as InfoCrop are considered more illustrative and there is one developed for India (Kumar et al., 2008). ...
... To date, out of some works such as those from Peiris et al., 2 which modeled the yielding in coconut palm in India, informations about yielding modeling are scarce. Therefore, the factors predicting the yielding in Côte d'Ivoire are unknown. ...
... The cumulative effect of the minimum temperature, rainfall and water deficit from 2010 on the subsequent yielding variation expression from 2013 was measured. In Sri Lanka, Peiris et al., 2 showed that, 94% fluctuations yielding were due among other things to rainfall. In Côte d'Ivoire, Traoré Sékou 3 revealed that the minimum temperature accounted for 65.14% variations monthly coconuts yielding. ...
... This study emphasized the potential benefits that can be gained through adaptation strategies. Coconut production forecasting studies have shown that annual coconut production is particularly sensitive to rainfall during January to March in the main coconut growing regions (Peiris et al., 2008). Further, maximum ambient temperature and relative humidity in the afternoon are the most significant variables in nut production (Peiris et al., 1997). ...
... Previous yield estimation and prediction studies for coconut in Sri Lanka which attempted to develop statistical relationships between annual yield and climatic factors were not conclusive due to the complex nature of coconut yield (Abeywardena, 1966(Abeywardena, , 1968Brintha et al., 2012;Peiris et al., 2008;Peiris et al., 2000;Peiris et al., 1997;Peiris et al., 1995;Peiris, 1998;Peiris, 1991Peiris, -1993. Process based models such as InfoCrop are considered more illustrative and there is one developed for India (Kumar et al., 2008). ...
... The government or policymakers should concentrate a major emphasis on these variables to support the overall development of the cropping system under consideration (Rathod and Mishra 2018). Peiris et al. (2008) predicted coconut production in Sri Lanka using seasonal climate information. Mijinyawa and AkpenPuun (2015) observed in Kwara State, Nigeria that the impact of climate on crop yield was significant for maize and rice yield at 95% probability level while the impact of climate on the yield of millet, sorghum and cowpea was insignificant. ...
A timely and reliable system of maize yield forecasting well in advance is prime emphasis to farmers and other people who are dependent on cereal crop. The best model was generated using maize field experiment trial which was conducted at Dorbasta union of Gobindagonj upazila, Gaibandha during two consecutive Rabi crops growing season 2018-19 and 2019-20. Randomized Complete Block Design (RCBD) along with five treatments (or varieties) and three replications were considered for maize yield performance. The agronomical and weather parameters and also, satellite data (Landsat 8 OLI) were used for the required maize field experiment. We found that Normalized Difference Vegetation Index (NDVI) was strongly positively correlated with the weather variables in this study. Stepwise regression method was applied for generating best estimated model. Best estimated model (Backward elimination) showed that only five controlled variables which were variety 5 (BHM 13), 1000 grain weight, diameter of cob, plant height and NDVI that were factors to the yield of maize. The developed maize yield forecast model (ideal model) including agronomical, weather and satellite data give the better results of yield estimation at regional level on the basis of best model criterion. Therefore, the ideal model used in specific region including all types of data that gives more precise result on maize yield or production that should be more significant and reliable in national level. So, the researcher, policymaker can use this maize yield prediction model forty to fifty days earlier of harvesting time.
... In these cases, the integrated part of the ARIMA model is equal to p, the order of the autoregressive part, and q, the order of the moving average. There are many studies that used the Arima model and developed its equations [21][22][23][24][25][26][27][28][29]. The first part is Autoregressive model. ...
The study aimed to compare ARIMA and Holt's models for predicting coconut metrics in Kerala. The coconut data series was collected from the period 1957 to 2019. Of this, 80% of the data (from 1957 to 2007) is treated as training data, and the rest (20% from 2008 to 2019) is treated as testing data. Ideal models were selected based on lower AIC and BIC values. Their accuracy was evaluated through error estimation on testing data, revealing Holt's exponential, linear, and ARIMA (0,1,0) models as the bestfit choices for predicting coconut area, production, and productivity respectively. After using the testing data, we tried for the forecasting for 2020-2024 using these models, and the DM test confirmed their significant forecasting accuracy. This comprehensive analysis provides valuable insights into effective prediction models for coconut-related metrics, offering a foundation for informed decision-making and future projections.
... Climate change will have a greater impact on non-plantation agriculture since the vast majority of farmers are small holders who mostly produce rice (Esham & Garforth, 2013). In the growing regions, coconut output is also sensitive to rainfall and dry spells (Peiris, et al., 2008). Changes in monsoon rainfall patterns and increases in maximum air temperature are two significant variables impacting the variability of coconut output in the major coconutgrowing regions (Peiris, et al., 2004). ...
Sri Lanka is vulnerable to the impacts of climate change due to its geographic location, size, and socioeconomic conditions. The country is exposed to a range of climate hazards including floods, landslides, droughts, cyclones, and sea-level rise. Therefore, numerous studies have been conducted to assess the island's vulnerability to climate change and its unprecedented consequences. These consequences are in a slow or rapid-onset nature. Sri Lanka also has a substantial policy context to build resilience and enhance the adaptability of vulnerable communities. Therefore, the literature plays an important role in formulating climate change policies as it provides a wealth of knowledge and insights into the impacts of climate change on human society and the environment. However, the literature is currently scattered, and no single source aggregates such scattered literature. Hence, this article reviews the climate change related literature in Sri Lanka, summarizes, and organises to assist future studies concerning the climate change context in Sri Lanka. JEL: Q54, Q56, Q58
... Long-term coconut cultivation will degrade soil quality due to skipping recommended practices such as fertilization, soil, and moisture conservation methods leading less coconut yield [5,6] . Most of the Sri Lankan agricultural soils show deficiencies in available nutrient contents including nitrogen, potassium, phosphorus, boron, sulfur, calcium, and magnesium, but toxicity in iron [7] . Since majority of local coconut-growing soils are sandy, such incidences can occur regularly [1] . ...
Coconut, as one of the main components of the daily Sri Lankan diet and one of the predominant crops grown in different agroecological zones except in higher elevations, has become a major export-earning crop. Its productivity is limited by adverse climatic changes in coconut growing areas, biotic and abiotic stresses, and poor agronomic practices. Cover cropping has been identified as a rewarding and time-tested farming solution that increases the productivity of most coconut land while addressing the above issues. It is the practice of growing plants for modifying soil properties, controlling pests and diseases, facilitating crop growth and yield, reducing chemical dependency, enabling the coconut-animal farming system, and generating extra profit. Legumes, fodder and pasture grasses have been identified as the common and trending cover crops for Sri Lanka. Comprehensive knowledge of selecting suitable cover crops, planting materials and proper agronomic practices are important for a successful cover cropping system under coconut. Farmers avoid this practice due to their ignorance on cover cropping, and its benefits, and due to the aggressive characteristics of cover crops such as reappearing behaviour, being an alternative host for pathogens, and competitiveness for natural resources. In this review, the unique attributes of common cover crop species are explained. Furthermore, the variety of on-farm benefits and ecosystem services of cover cropping and some important agronomic considerations are reviewed. Finally, future research potential for recommending new species and their adaptability to a wide range of ecological and ecosystem circumstances under coconut cultivation are investigated.
... Among the environmental variables, weather has a significant impact on crop output potential. Though, a number of regression models based on weather parameters have been used to predict coconut yield [3,4] (Mahesha et al., 1992), but comparison of multiple statistical models has received very much less attention. Multiple linear regression (MLR), on the other hand, is appropriate for smaller datasets, but its application is limited when the number of predictors exceeds the number of samples [5]. ...
Coconut is the world's most significant plantation crop, and it is grown in practically every country. As Coimbatore is the leading producer of coconut in Tamil Nadu, followed by Thanjavur and Kanyakumari, this study is centred on the Coimbatore area. West Coast Tall is a popular cultivar that produces more than other types. The West Coast Tall (WCT) cultivar was used in this research. In this paper, four models were developed such as Ridge, Least Absolute Shrinkage Selection Operator (LASSO), Elastic net (ELNET) regression methods and Artificial Neural Networks (ANN). Further, we validate this model using field-level data from TNAU coconut research farm for two years. The purpose of this communication is to find the best fit model for prediction of coconut yield using weather parameters and external factors in Coimbatore district. The models were selected based on different performance metrics such as RMSE, MAPE, MAE, and R2. Among the four models developed in the study, the ELNET model is found to be best model for prediction of coconut yield based on weather and external factors for the available data in the studied region.
... Coconut production in the current year is highly dependent on the rainfall pattern of the previous year. The rainfall during February, June, July, September and December in the preceding year to the harvest is critical for the following year's production (Peiris et al., 2008). According to the rainfall records of Kurunegala District in 2016 and 2017, it is clear that the monthly rainfall of the critical months mentioned previously were below the average with implications for production drop (since Kurunegala District production statistics are not available, ANCP is taken as a proxy) in 2017 and 2018 ( Figure 38). ...
The twin challenges of climate change and the Covid-19 pandemic are food system drivers capable of influencing the food system outcomes comprising food security, social welfare and environmental capital. The impact of climate change in the context of a severely weakened
economy due to Covid-19 has exacerbated the risks of food availability and accessibility across
the country especially, to the poor and vulnerable communities in the city region.
This assessment examines the impact of climate change and Covid-19 on the Colombo City
Region Food System (CRFS) to identify existing food system vulnerabilities and provide
information to cope with these shocks and uncertainties to build a resilient food system. The
CRFS assessment considers rural-urban linkages including foodsheds that feed Colombo. The
five food supply chains; paddy/rice (Ampara, Anuradhapura, Polonnaruwa); coconut
(Kurunegala, Puttalam, Gampaha); upcountry vegetables (Nuwaraeliya, Badulla) low country
vegetables (Anuradhapura); and the Southern and Western fishing belts representing the
Colombo CRFS were assessed.
In this research, the risk assessment framework specified in the IPCC Fifth Assessment Report
(AR5) was used as a guide. A mixed approach was used to collect data consisting of an
extensive web-based search of climate change impact-related media coverage over six years
(2015-2020), secondary data, literature and document review, stakeholder interviews and
expert consultation. The individual events identified were used to determine the nature of the
hazard, exposure and vulnerability. The data were analyzed to derive risk/vulnerability indices
and degrees of risk/vulnerability at individual supply chain nodes.
The major climate hazard that has impacted the three selected rice-producing districts viz.,
Ampara, Anuradhapura and Polonnaruwa is drought. There is a clear relationship between the
intensity of the climate hazard and paddy production in these districts. Among the three
districts, Anuradhapura is more vulnerable and carries higher risk. The paddy production drop
in 2017 attributed to drought created a shortfall in rice for local consumption prompting the
importation of significant volumes of rice. This is a clear indication of the impending threat of
climate change to food self-sufficiency in Sri Lanka. Paddy storage, milling and wholesale
operations are mainly in the hands of the private sector. The government agency mandated to
oversee the paddy and rice supply chain operations plays a sedentary role. Climate change has
threatened the operations of small- and medium-scale rice mills as a short supply of paddy
makes these mills redundant. Climate change can be considered as one of the main contributors
to rice price volatility. There are a noteworthy number of reports of confiscating significant
amounts of spoiled rice stocks in the media. This also could contribute to price fluctuation.
This warrants further investigation as significant levels of spoilage will have implications for
rice prices and food security. Frequent price fluctuations can impact rice consumption and thus
food energy intake and protein intake of the poorer segments in the Colombo city region. There
is serious institutional fragmentation and a disconnect among public and private sector
stakeholders in the rice sector. Therefore, it is necessary to develop a coordination mechanism
involving relevant stakeholders from the public and private sectors.
... While the lifespan of coconut trees ranges from 50 to 100 years depending on variety, productivity peaks at 50 % of lifespan (Foale 2003). Also, productivity is often disturbed by pest attacks (e.g., rhino beetle, hispid beetle, Sexava spp.), and disease (e.g., bud rot disease, nut fall disease) (Suriya 2016, Alouw & Wulandari 2020; and is also influenced by climate and seasonal variability of weather (Peiris et al. 2008). The fluctuating prices of pesticides and herbicides have led the farmers of Pulau Burung to favour mechanical methods of pest control over chemical ones (Aumora et al. 2016). ...
Catastrophic forest and land fires have turned a global spotlight on tropical peatlands in Indonesia. The consequent reformulation of Indonesian land-use policy has been complicated by the existence of two apparently opposing objectives, namely conservation and development. In many regions of the country, peatland agriculture is one of the pillars of development, but its sustainability is questioned. This article focuses on a coconut plantation managed by local stakeholders on peatland in Pulau Burung District (Riau Province) where excellent productivity has been achieved, promoting Indonesia as the world's largest supplier of coconut. Quantitative and qualitative approaches are employed to examine the sustainability of this agricultural system over a period of 32 years; in terms of water management, land management and socioeconomic development. The fundamental determinant of sustainability in this system is integrated water management which confined the annual range of water table fluctuations in 2018–2019 to 25–71 cm below ground surface. The resulting long-term rate of subsidence is 1.7 cm yr-1, or up to 80 % lower than reported from peatland agriculture elsewhere. The combination of water management, land management and shared understanding in Pulau Burung means that the risk of peat fires is minimal. In 2013–2018, the plantation produced coconuts at an average rate of 6,500 ha-1 yr-1. This converts to the standard international unit of copra productivity as 1.3 t ha-1 yr-1, which is 55 % higher than the productivity achieved by Indonesia as a whole and 20 % higher than the corresponding global figure. A significant socioeconomic outcome has been the elevation of a village to a district encompassing 14 villages, with attendant benefits for local people in terms of living conditions and improved access to food, goods, services and education. We conclude that the integrated water management and interdependence of local stakeholders in Pulau Burung has managed to balance the peatland's role in developing human welfare with maintenance of the hosting environment over several decades.
... Therefore, the non-linear regression approach which facilitates the relationship using the same input dataset for linear regressions is gaining popularity. The impact of rainfall and climate change on crop yield was previously studied for major crops such as paddy (De Silva et al., 2007;Dharmarathna et al., 2012), maize (Karunaratne and Wheeler, 2015) and plantation crops (Peiris et al., 2007;Wijeratne et al., 2007) in different geographic locations and timescales in Sri Lanka. However, a detailed analysis among climatic factors and the paddy yield has not been carried out. ...
Purpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka.
Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen’s slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield.
Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3.
Research limitations: Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study.
Originality/value: We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka.
... Following chart (Fig 3) shows Sri Lanka's domestic coconut demand was fluctuating between 2,000 million nuts and 2,500 million nuts per annum. Accurate forecasting of annual national coconut production in 2005 was 2,715 million nuts (Peiris, et al., 2008) when the domestic coconut demand was 2,047 million nuts. The average price within the time 1995 to 2016 was varying between Rs. 8.40 to Rs. 51.2 (Fig. 4). ...
Coconut is the second most important food and it is a highly demanded agricultural commodity in Sri Lanka. Supply shock and the price fluctuation is one of the major problem faced by coconut consumer. Therefore, estimating coconut demand is a needful topic to predict coconut production. The study estimated the domestic coconut income demand elasticity, uncompensated and compensated price elasticities using the Almost Ideal Demand System (AIDS). Secondary time-series data from 1995 to 2016 were collected from secondary sources. Coconut consumption was fluctuating and the price showed an increasing trend throughout the years. Further, the demand was relatively the same for any price held in time. Estimated income (expenditure) elasticity of coconut is 0.825; the Marshallian (uncompensated) and Hicksian (compensated) price elasticity of coconut are 0.758 and 0.725 respectively. Indicate that, the price and expenditure demand for coconut was inelastic. Therefore, it is considered as an essential good in Sri Lanka. However, the demand would increase shortly with the population growth in Sri Lanka. Hence, it is necessary for increasing the coconut production in the country.
... In another example, the national coconut production of Sri Lanka was forecast through prediction in seven production regions based on climate variables [87]. The absolute error on production estimates for 2003 and 2004 was 6.5 and 7.0%, respectively. ...
The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.
... Ranagalage et al. (2017) have located the environmentally critical metropolitan regions in Sri Lanka based on land surface temperature (LST) and normalized difference vegetation index (NDVI) relationship. Impact of climate change on different crop yield in Sri Lanka are evidenced during historical (Karunaratne and Wheeler 2014; Peiris et al. 2004Peiris et al. , 2008Peng et al. 2004;Zubair 2002) as well as in future time periods (Karunaratne and Wheeler 2014). Climate change impact is prominent and wide agro-ecological variations are found in Sri Lanka in recent decades. ...
The human–environment interactions are entwined across different spatio-temporal scales. The prime focus of this study is to investigate the wide range of changes happening in seasonal maximum and minimum temperatures (Tmax and Tmin) during 1950–2012 over the island country Sri Lanka in the deep tropics and to analyse associated important drivers explaining these changes. Fingerprint-based detection and attribution (D&A) analysis formally decipher the rudimentary causes of climate change by investigating the extent to which pattern of response to anthropogenic forcing (i.e., fingerprints) from climate model simulations explains the observed changes. Coupled Model Intercomparison Project Phase-5 experiment simulations are utilized to perform fingerprint-based D&A analysis for the first time in Sri Lanka. The PiControl experiment simulations which include only natural internal variability of climate could not explain the observed changes in seasonal Tmax and Tmin. However, the unequivocal attribution to human‐induced climate change (historical GHG and historical experiment simulations) was not possible in most of the cases except a few. Even though climate change impact is prominent in extra-tropics, an unusual human-induced climate change signature in deep-tropics is manifested in the present study. Research highlights: Fingerprint-based formal detection and attribution approach is utilizedObserved temperature changes are not due to natural internal climate variabilityClimate change impact is prominent in deep-tropicsChange in temperature is significant over whole of Sri LankaLarge-scale atmospheric circulation patterns have a strong influence on hydroclimatology of Sri Lanka
... In addition, some saturated fatty acids could be available, as lauric acid that can prevent atherosclerosis. Moreover, coconut being one of the multipurpose and vital food items for millions of inhabitants of the south and south-east Asia and the Pacific islands, it constitutes one of the most sought-after ingredients, when mixed in most recipes prepared in Sri Lanka [5]. Coconut milk is a milky liquid obtained by manual or mechanical extraction of fresh coconut kernel with or without the addition of water. ...
Background: Being available in Sri Lanka, Ready-to-serve drinks are more popular among Sri Lankan’ consumers . Further, ready-to-serve organic fruit drinks are nowadays becoming more popular due to more concern about healthy living. Aim: To produce a ready-to-serve drink using pineapple juice with coconut milk. Methods and Material: Pineapple Juice (Ananas comosus) and Coconut milk were optimized to a blended ready to serve beverage which was mixed in four different predetermined ratios and stored for 14 days in glass bottles (200ml capacity). Physicochemical and sensory analysis were done according to the standards procedures. After 14 days of incubation period, four samples were tested for their sensory properties at CBL Natural foods laboratory. Results: The ratio of pineapple juice: coconut milk (71:29) was ranked as highest score (106) for sensory evaluation and content Total suspended solids (13 ºBrix), pH (4.25) and moisture (82.32). Conclusions: The formulation of mixed blend Pineapple juice beverage is possible to satisfy consumer tastes and preferences. Keywords: Ready-to-serve drink, Pineapple juice, Coconut milk, Physicochemical, Sensory properties.
... Coconut production is closely linked to the rainfall distribution pattern of previous years. Therefore, when rainfall is considered as the most important determinant of yield, lagging rainfall is often used in coconut yield prediction studies (Peiris et al., 2008). ...
... Weather affects all stages of the long development cycle extending to 44 months, and thus there is likely to be extended predictability based on climate variability. Hence Coconut Development Board is undertaking an were being made earlier to predict coconut production using climate data such as rainfall (Abeyawardena, 1968) and later by Peiris et al. (2008) in Sri Lanka. Similarly, in India, attempts were made using climatic parameters (Krishnakumar, 2011). ...
... Weather affects all stages of the long development cycle extending to 44 months, and thus there is likely to be extended predictability based on climate variability. Hence Coconut Development Board is undertaking an were being made earlier to predict coconut production using climate data such as rainfall (Abeyawardena, 1968) and later by Peiris et al. (2008) in Sri Lanka. Similarly, in India, attempts were made using climatic parameters (Krishnakumar, 2011). ...
... Weather affects all stages of the long development cycle extending to 44 months, and thus there is likely to be extended predictability based on climate variability. Hence Coconut Development Board is undertaking an were being made earlier to predict coconut production using climate data such as rainfall (Abeyawardena, 1968) and later by Peiris et al. (2008) in Sri Lanka. Similarly, in India, attempts were made using climatic parameters (Krishnakumar, 2011). ...
Coconut has a prolonged reproductive phase of 44 months from the initiation of the inflorescence primordium to full maturity of
the nuts. Weather affects all stages of the long development cycle, and thus there is likely to be extended predictability based on
climate variability. Arasikere taluk of Hassan district, which has a major share of coconut area of Karnataka state, is frequently
experiencing deficit rainfall coupled with a decline in groundwater level. Hence, an attempt was made to relate the coconut
sample survey data of Coconut Development Board with climate data of Arsikere taluk of Hassan district. Mean nut yield per
palm, per year in the Arasikere taluk was 49.2. Among the villages, Gijihalli recorded significantly lower nut yield (42.2) followed
by Jajur (48.8) and Aggunda (55.3). Mean maximum, and minimum temperature during 2010-2017 was 32.40
C and 19.50
C
respectively, with an average annual rainfall of 840 mm. Annual rainfall during 2011, 2012 and 2016 was below normal compared
to other years. Correlation of monthly nut yield per palm with rainfall showed a significant positive correlation with the previous
three to four years rainfall. Long dry spell during primordial initiations to nut maturity during consecutive two years 2011 and
2012 has resulted in significantly low nut yield during 2014. Rainfall during 2013 and 2014 was comparatively better, resulting
in significantly higher nut yield during 2016 compared to 2014. Among the different stages, the primordium initiation stage and
the ovary development stages were more strongly and significantly influenced by the weather parameters during all the years.
Rainfall during button development stage followed by Tmax and rainfall during the spadix emergence stage showed a significant
contribution to the weather-based regression model. Future climate of Arasikere showed an increase in annual rainfall mainly
during September and October but declined during November/December period. Maximum and minimum temperature showed
an increase by 1-1.50
C which may increase the evaporative demand and dry spell duration resulting in moisture stress thus
highlighting the importance of rainwater harvesting to take advantage of increased rainfall under future climatic condition. The
future climate scenario may also favour the attack of pests like eriophyid mite.
... Weather affects all stages of the long development cycle extending to 44 months, and thus there is likely to be extended predictability based on climate variability. Hence Coconut Development Board is undertaking an were being made earlier to predict coconut production using climate data such as rainfall (Abeyawardena, 1968) and later by Peiris et al. (2008) in Sri Lanka. Similarly, in India, attempts were made using climatic parameters (Krishnakumar, 2011). ...
Coconut has a prolonged reproductive phase of 44 months from the initiation of the inflorescence primordium to full maturity of
the nuts. Weather affects all stages of the long development cycle, and thus there is likely to be extended predictability based on
climate variability. Arasikere taluk of Hassan district, which has a major share of coconut area of Karnataka state, is frequently
experiencing deficit rainfall coupled with a decline in groundwater level. Hence, an attempt was made to relate the coconut
sample survey data of Coconut Development Board with climate data of Arsikere taluk of Hassan district. Mean nut yield per
palm, per year in the Arasikere taluk was 49.2. Among the villages, Gijihalli recorded significantly lower nut yield (42.2) followed
by Jajur (48.8) and Aggunda (55.3). Mean maximum, and minimum temperature during 2010-2017 was 32.40C and 19.50C
respectively, with an average annual rainfall of 840 mm. Annual rainfall during 2011, 2012 and 2016 was below normal compared
to other years. Correlation of monthly nut yield per palm with rainfall showed a significant positive correlation with the previous
three to four years rainfall. Long dry spell during primordial initiations to nut maturity during consecutive two years 2011 and
2012 has resulted in significantly low nut yield during 2014. Rainfall during 2013 and 2014 was comparatively better, resulting
in significantly higher nut yield during 2016 compared to 2014. Among the different stages, the primordium initiation stage and
the ovary development stages were more strongly and significantly influenced by the weather parameters during all the years.
Rainfall during button development stage followed by Tmax
and rainfall during the spadix emergence stage showed a significant
contribution to the weather-based regression model. Future climate of Arasikere showed an increase in annual rainfall mainly
during September and October but declined during November/December period. Maximum and minimum temperature showed
an increase by 1-1.50C which may increase the evaporative demand and dry spell duration resulting in moisture stress thus
highlighting the importance of rainwater harvesting to take advantage of increased rainfall under future climatic condition. The
future climate scenario may also favour the attack of pests like eriophyid mite.
... In addition, some saturated fatty acids could be available, as lauric acid that can prevent atherosclerosis. Moreover, coconut being one of the multipurpose and vital food items for millions of inhabitants of the south and south-east Asia and the Pacific islands, it constitutes one of the most sought-after ingredients, when mixed in most recipes prepared in Sri Lanka [5]. Coconut milk is a milky liquid obtained by manual or mechanical extraction of fresh coconut kernel with or without the addition of water. ...
Being available in Sri Lanka, Ready-to-serve drinks are more popular among Sri Lankan’ consumers . Further, ready-to-serve organic fruit drinks are nowadays becoming more popular due to more concern about healthy living. AIM: To produce a ready-to-serve drink using pineapple juice with coconut milk. METHODS AND MATERIAL: Pineapple Juice (Ananas comosus) and Coconut milk were optimized to a blended ready to serve beverage which was mixed in four different predetermined ratios and stored for 14 days in glass bottles (200ml capacity). Physicochemical and sensory analysis were done according to the standards procedures. After 14 days of incubation period, four samples were tested for their sensory properties at CBL Natural foods laboratory. RESULTS: The ratio of pineapple juice: coconut milk (71:29) was ranked as highest score (106) for sensory evaluation and content Total suspended solids (13 ºBrix), pH (4.25) and moisture (82.32). CONCLUSIONS: The formulation of mixed blend Pineapple juice beverage is possible to satisfy consumer tastes and preferences.
... Notably, changes of weather parameters during the postfertilization phase of coconut affect the growing bunches in various degrees nearly for a 11-month duration (Abeywardena, 1956) directly reducing the nut yield of coconut. Literature provides much evidence for the effect of weather parameters on coconut production Peiris et al., 2008;Peiris and Peries 1993;Peiris and Thattil, 1997), however, the studies on the effect of extreme weather events on coconut production are scarce (Pathmeswaran et al., 2018). Coconut has been cultivated in Sri Lanka as a commercial crop since the 17 th century and the extent under coconut was rapidly increased in the colonial era reaching into a peak in 1960s. ...
... Arasikere taluk of Hassan district is experiencing deficit rainfall frequently coupled with decline in ground water level. Advance knowledge of coconut production at national and regional scales is useful for planning within the industry and in this direction attempts were being made earlier to predict coconut production using climate data such as rainfall by Abeyawardena as early as 1968 (Abeyawardena, 1968) and later by Peiris et al., (2008) in Sri Lanka. Similarly in India also attempts were made using climatic parameters (Krishna Kumar, 2011). ...
... Olsen and Goodwin (2005) carried out a statistical survey on Oregon hazelnut production. Peiris et al., (2008) predicted coconut production in Sri Lanka using seasonal climate information. Mayer and Stephenson (2016) carried out statistical forecasting of Australian macadamia crop. ...
... Much of the climate research in Sri Lanka has been focused on the analysis of climatic variations over the past several decades, predicting the future climatic changes (de Costa, 2008;Jayawardene et al., 2015) and their impacts on agriculture and the adaptation strategies needed for a specific sector and for food security (Wijeratne et al., 2007a;Peiris et al., 2008;Esham & Garforth, 2013). However, very little attention has been paid to the impact of climate change on species and ecosystems in Sri Lanka (Miththapala, 2015). ...
The climate change impacts are felt by all facets and sectors of ecosystems, covering flora, fauna and
environment. Sri Lanka is considered as a vulnerable, small island country that is under serious threat from
climate change impacts. The most profound impacts of climate change in Sri Lanka will be on agriculture and
food security, water and coastal resources, biodiversity changes, and human health. Sri Lanka’s biodiversity
is significantly important both on a regional and global scale as it has the highest species density for flowering
plants, amphibians, reptiles, and mammals. Sri Lanka’s varied ecosystems provide many services that are of
significant economic value and play a crucial role in providing goods and ecosystem services. The subsequent
sections featuring specific aspects of biodiversity in forests, freshwater wetlands, coastal and marine systems
and agricultural systems, provide greater detail on the ecosystem services and bio-resources. Habitat loss
and fragmentation, invasive alien species, deforestation and forest degradation, development projects,
environmental pollutions and climate change (global warming) are the major threats to the biodiversity of
the country. Climate change impacts on environment lead to a reduction in the distribution and abundance
of species, especially endemics, which may even result in their global extinction. The introduction of
various policies and guidelines in relation to environment is a good sign for conservation of ecosystems and
biodiversity. The government of Sri Lanka has been implementing various environmental projects aiming at
reducing deforestation and degradation of ecosystems. Policies and measures already developed under such
initiatives will no doubt preserve natural habitats for plant and animal species. However, being a developing
country with many economic challenges, the funds and expertise available for monitoring climate change
impacts and biodiversity conservation are not sufficient.
... Sri Lanka is expected to have a shortfall in meeting its local coconut demand by 2040 due to changes in rainfall patterns and a rise in maximum air temperature-two critical factors influencing coconut productivity (Peiris et al. 2004(Peiris et al. , 2008. Other food crops such as fruits and vegetables in which Sri Lanka has a high level of self-sufficiency will be affected by climate change; however, there is no published literature on the extent of the impact on their productivity. ...
There is growing concern in Sri Lanka over the impact of climate change,
variability and extreme weather events on food production, food security and livelihoods.
The link between climate change and food security has been mostly explored in relation to
impacts on crop production or food availability aspects of food security, with little focus on
other key dimensions, namely food access and food utilization. This review, based on
available literature, adopted a food system approach to gain a wider perspective on food
security issues in Sri Lanka. It points to several climate-induced issues posing challenges
for food security. These issues include declining agriculture productivity, food loss along
supply chains, low livelihood resilience of the rural poor and prevalence of high levels of
undernourishment and child malnutrition. Our review suggests that achieving food security
necessitates action beyond building climate resilient food production systems to a holistic
approach that is able to ensure climate resilience of the entire food system while addressing
nutritional concerns arising from impacts of climate change. Therefore, there is a pressing
need to work towards a climate-smart agriculture system that will address all dimensions of
food security. With the exception of productivity of a few crop species, our review
demonstrates the dearth of research into climate change impacts on Sri Lanka’s food
system. Further research is required to understand how changes in climate may affect other
components of the food system including productivity of a wider range of food crops,
livestock and fisheries, and shed light on the causal pathways of climate-induced nutritional
insecurity.
... The experimental sites are located in the low country intermediate zone (IL 1a ) (Punyawardane, 2008). Generally, these areas receive the highest rainfall during October to December and are prone to moderate to severe droughts during February to September (Peiris et al., 2008). The plantations are maintained with agricultural practices recommended by CRISL. ...
High sensitivity of pollen and ovules to stress, particularly around meiosis, is one of the major attributes for failures in fruit set in many crops. This study assessed the influence of heat (monthly mean Tmax>33ºC) and water stress (monthly rainfall<90 mm) at stages around meiosis on the quality of female (Sri Lanka Green Dwarf, SLGD) and male (Sri Lanka Tall (SLT) and San Ramon (SR)) flowers and their fruit set (SLGD x SLT and SLGD x SR) with similarly stressed parents and with stressed vs non-stressed parents (reciprocal pollination) under controlled hand pollination. Flowers were sampled to represent their development under eight different stress levels; six with heat and/or water stress at stages around meiosis (flowers opened in March, June and September in two years) and two controls without stress around meiosis (flowers opened in December). Female flowers of emasculated SLGD palms opened in selected eight months were pollinated with SLT and SR pollen produced in the same month and produced before three months to accomplish reciprocal pollination. Quality of flowers, number of female flowers and fruit set (FS) were monitored. Unstressed pollen had significantly higher germination (PG %), tube growth (PTL) and starch and, female flowers had higher starch content compared to flowers stressed at any stage around meiosis. Water stress particularly at the meiosis stage increased the total soluble sugars (TSS) in pollen and female flowers. The FS was significantly higher (80%) in unstressed parents compared to stressed parents. The failures in fruit set between stressed parents could be minimized by using non stressed pollen to pollinate stressed female flowers. Of the two crosses the early fruit set of SLGD x SR hybrid was greater compared to that of SLGD x SLT. Whilst female flower number (R 2 =0.62) and PTL (R 2 =0.54) were significantly (p<0.05) influenced by the cumulative rainfall during final four months prior to flower opening, pollen and female flower starch (R 2 =-0.61, R 2 =-0.67) was affected by mean Tmax of the same period. FS% showed the best correlation with starch of female flowers (R 2 =0.78). The study concluded that heat and/or water stress around meiosis is very critical for reproductive organs and early fruit set in hybrid seeds. It also revealed an important strategy to minimize the failures in fruit set of dwarf x tall seed coconuts during stressed months, by using non stressed pollen to pollinate the stressed female flowers in controlled hand pollination.
... Efficient application of climate services requires that climate information become integrated into sectoral policies and operations particularly in water resources. This requires impact assessment in diverse fields such as water resources management (Chandimala and , malaria risk mitigation , coconut production (Peiris et al., 2008). Climate services also require capacity building (Zubair, 2004a) and ...
A hydro-climatic advisory has been developed as the outcome of a collaborative research project on producing useable climate information for water resources management in a decade-long collaboration between the Mahaweli Authority of Sri Lanka (MASL) and the International Research Institute for Climate and Society (IRI) at Columbia University. Our institute, the Foundation for Environment, Climate and Technology (FECT), grew out of this and subsequent projects. Over the last four years, FECT has produced weekly hydro-meteorological advisories for Sri Lanka for the Water Management Secretariat (WMS) of MASL. The WMS convenes a multi-institutional panel for water management and this advisory is consulted at its weekly water management panel. This hydro-meteorological advisory incorporates inputs from Sri (NCEP) of National Oceanic and Atmospheric Administration (NOAA). The monitoring section of the report provides satellite derived rainfall estimates at daily, decadal (10-day) and monthly rainfall estimates, weekly average Sea Surface Temperature anomalies. The prediction section is based on predictions from IMD, NOAA and NCEP and, seasonal predictions from IRI, European Center for Medium Range Weather Forecast and APEC Center. These predictions are from 3 days to 3 months ahead from the above agencies and scientists including our own. Objective skill assessments are underway for these predictions. We have provided these advisory to the MASL water panel over the last four years and have consistently been encouraged by the Mahaweli water managers. This product is available at our websites http://www.climate.lk and directly at http://fectsl.blogspot.com. Further enhancements of this advisory is under development. Following the success of this work, at the request of the Maldivian Ministry of Environment, we are now providing a monthly advisory for Maldives with the Maldivian Meteorological Service at
... Climate change is an over-arching driver of biodiversity loss that is predicted to be the dominant driver of loss by the end of this century (MEA, 2005). Much of climate change research in Sri Lanka has focussed on a) predicting the climatic changes in Sri Lanka (Basanyake, 2007; de Costa, 2008; Jayawardene et al., 2015; Zubair and Ropelewski, 2006; Zubair et al., 2007); b) examining the impacts of climate change on natural hazards (Zubair et al., 2006b); and c) predicting impacts of climate change on agriculture and the adaptive strategies needed for the sector and for food security (inter alia, Athulathmudali et al., 2011; De Costa, 2000; Eriagama et al., 2010; Esham and Garforth, 2013; Fernando, 2000; Panabokke and Punyawardena, 2010; Peiris et al., 2008; Wijeratne,2007; Zubair, 2002). Very little attention has been paid to the impacts of climate change on species and ecosystems. ...
This paper proposes that Sri Lanka’s nature conservation efforts are hampered by the lack of a clear understanding of the meaning of the word conservation. This lack of understanding then impedes effective implementation of conservation actions. It revisits terminology to obtain clarity of the definition of conservation. Inherent in many current definitions are the following. 1) Humans are integral to conservation biology. Anthropogenic activities drive the loss of biodiversity, necessitating conservation, but humans must be a part of the solution; 2) Preservation, maintenance, enhancement, restoration and sustainable use are all elements of conservation. The difference between preservation and conservation is clarified. The paper assesses gaps in current conservation measures, as: 1) lack of practice of true conservation in Sri Lanka that includes all its elements; 2) lack of focus on landscape-scale conservation; 3) lack of focus outside protected areas; 4) lack of negotiation with decision-makers using a tender that is understood by them; 5) lack of congruence between conservation knowledge and conservation practice; 6) complacency with regard to Red Listing™; 7) inadequate prioritisation of conservation research; 8) inadequate predictive research; 9) lack of research on the impact of climate change on species and ecosystems; and 10) focus on a sectoral, rather than a holistic approach. The paper concludes by providing recommendations for future actions.
... Climate change is an over-arching driver of biodiversity loss that is predicted to be the dominant driver of loss by the end of this century (MEA, 2005). Much of climate change research in Sri Lanka has focussed on a) predicting the climatic changes in Sri Lanka (Basanyake, 2007;de Costa, 2008;Jayawardene et al., 2015;Zubair and Ropelewski, 2006;Zubair et al., 2007); b) examining the impacts of climate change on natural hazards (Zubair et al., 2006b); and c) predicting impacts of climate change on agriculture and the adaptive strategies needed for the sector and for food security (inter alia, Athulathmudali et al., 2011;De Costa, 2000;Eriagama et al., 2010;Esham and Garforth, 2013;Fernando, 2000;Panabokke and Punyawardena, 2010;Peiris et al., 2008;Wijeratne,2007;Zubair, 2002). ...
This paper proposes that Sri Lanka’s nature conservation efforts are hampered by the lack of a clear understanding of the meaning of the word conservation. This lack of understanding then impedes effective implementation of conservation actions. It revisits terminology to obtain clarity of the definition of conservation. Inherent in many current definitions are the following. 1) Humans are integral to conservation biology. Anthropogenic activities drive the loss of biodiversity, necessitating conservation, but humans must be a part of the solution; 2) Preservation, maintenance, enhancement, restoration and sustainable use are all elements of conservation. The difference between preservation and conservation is clarified. The paper assesses gaps in current conservation measures, as: 1) lack of practice of true conservation in Sri Lanka that includes all its elements; 2) lack of focus on landscape-scale conservation; 3) lack of focus outside protected areas; 4) lack of negotiation with decision-makers using a tender that is understood by them; 5) lack of congruence between conservation knowledge and conservation practice; 6) complacency with regard to Red Listing™; 7) inadequate prioritisation of conservation research; 8) inadequate predictive research; 9) lack of research on the impact of climate change on species and ecosystems; and 10) focus on a sectoral, rather than a holistic approach. The paper concludes by providing recommendations for future actions.
The coconut crop (Cocos nucifera L.) is essential in humid tropical regions, contributing to the economy and food security. However, its perennial nature makes it sensitive to climate variability, particularly extreme events that affect productivity. This study evaluated the impacts of extreme climatic events on the productivity of dwarf green coconut in northeastern Pará, analyzing rainy (PC—December to July) and less rainy (PMC—August to November) periods between 2015 and 2023. Meteorological and experimental data were used, including extreme climate variables such as maximum temperature (HT) and precipitation (HEP), defined by the 90th percentiles, and low precipitation (LP, 10th percentile). Predictive models, such as Multiple Linear Regression (MLR) and Random Forest (RF), were developed. RF showed better performance, with an RMSE equivalent to 20% of the average productivity, while that of MLR exceeded 50%. However, RF struggled with generalization in the test set, likely due to overfitting. The inclusion of lagged productivity (productivity t-1) highlighted its significant influence. During the PC, extreme high precipitation (HEP) events and excessive water surplus (HE) occurring after the fifth month of inflorescence development contributed to increased productivity, whereas during the PMC, low-precipitation (LP) events led to productivity reductions. Notably, under certain circumstances, elevated precipitation can mitigate the negative impacts of low water availability. These findings underscore the need for adaptive management strategies to mitigate climatic impacts and promote stability in dwarf green coconut production.
The study attempts to forecast coconut production in major coconut-producing states in India. The future projections on coconut production have been calculated based on yearly data for 73 years (1949–50 to 2021–22) accessed from the database of Indiastat (2022). We have used prominent forecasting techniques for the purpose and a suitable model has been chosen based on the lowest results of MAPE. The damped linear trend has been chosen for forecasting coconut production in Karnataka whereas Differenced first-order Auto Regressive model with drift has been adopted for Kerala and Karnataka. This study has considered a large dataset compared to other existing works and has chosen states that produce coconut on a large scale in India. Along with this, this study also attempts to find which state will produce more nuts for the Indian coconut industry, which can help the concerned stakeholders to take necessary decisions. Future projections depict that Kerala will continue to be the largest producer of coconut and Karnataka will show remarkable performance in coconut production during the upcoming four years post-study period.
Coconut is the most important plantation crop, planted in nearly every country. Coimbatore is the major producer of coconut in Tamil Nadu, followed by Thanjavur and Kanyakumari, hence this research is focused on the Coimbatore district. West Coast Tall is the popular variety which gives better yield than other varieties. This study was carried out on West Coast Tall (WCT) variety. In this paper, Coconut yield prediction model is developed under weather and external factors such as Minimum temperature, Relative Humidity, Rainfall, Plant height, Stem girth, Female flowers in inflorescence, Leaf length and Copra content. Correlation and multiple regression analysis is carried out to obtain the final form of the yield prediction model. Further, we validate this model using field-level data from TNAU coconut research farm using MATLAB and python software. The actual yield in comparison with the predicted yield using software is found to be in satisfactory agreement. In addition, we developed forecasting model for coconut for forthcoming years using proposed model.
The objective of this study was to evaluate some production parameters for the identification of elite Nain. Twenty-seven coconut trees, spread over 2 plots, were selected. A control, Malay Yellow Dwarf (NJM), was added to each plot. The study of the relationship between five variables revealed highly significant correlations between them. The multivariate analyses made it possible to identify among the genotypes, the type 7 individuals as being the most efficient. Individuals of type 7 had the highest averages for number of flowers at bag laying (NFP), number of flowers at bag removal (NFE), number of knotted flowers (NFN) and number of nuts harvested (NNR) compared to types 4, 5 and 6. Pollen germination rates in type 1 (Control) individuals were significantly higher than those of the other four types that did not differ significantly between them. The tests for the prediction of the number of nuts harvested indicated that 48.31% of the fluctuations of seed nuts reaching maturity are attributable to the number of female flowers counted at the installation of the bag, that of the female flowers counted at the bag removal and that of the female flowers tied. The predicted values for the number of mature seed nuts are: Nnoirec = -1.12 – 0.007*Nflepose + 0.012*Nflenlev + 0.408*Nflnou +0.014TG. Individuals of type 7 likely to be elite broodstock can be used in programs of genetic improvement of the coconut for better performance.
The purpose of the research paper is to identify the prominent factors relevant to supply shortage and provide recommendations to ABC (Pvt) Ltd in order to overcome the shortage in the supply of coconuts and it will facilitate the company to cater the international market for coconut-based products. Researchers have developed what are the reasons for inadequate supply? and how the supply shortage can be reduced? as research questions in order to address the problem of supply shortage in coconuts compared to the international demand. Moreover, qualitative research followed by a phenomenological research strategy was conducted to solve the research problem and in-depth interviews with the internal and external parties of ABC (Pvt) Ltd were held for the data collection. Correspondently, thematic analysis was used to analyse the data. Finally, the research has identified prominent reasons for the lack of supply of coconuts and provided recommendations along with the incorporation of the partnership model. The applicability and the preferences of deploying partnership model as Supplier relationship Management (SRM) theory as and with the aim of reducing the sup shortage in coconuts in which it will assist ABC (Pvt) Ltd to satisfy the international demand for coconut-based products were discussed under the study.
Coconut (Cocos nucifera L.) is an important crop, mainly in the tropical and subtropical regions of the globe. It is one of the top ten useful trees in the world, providing food and non-food benefits to millions of people worldwide. As coconut is the second largest crop in extent next to staple crop 'Rice' in Sri Lanka, it plays a vital role in the household food security. The annual production of coconuts in the country is reported to be about 2.8 billion nuts, out of which 1.8 billion is used for household consumption, and the balance of 1 billion is being available for manufacture of coconut products. In recent times, factors like climate change, fragmentation of coconut lands, and prevalence of pest and diseases pose major risk for future coconut yield in the country. Maximizing the utilization of the coconut sector's by-products is proposed as a proactive approach to address coconut-based food insecurity in Sri Lanka. Coconut shell, coconut testa, coconut sap of the inflorescence, and mature coconut water released from factories are some of the by-products of coconut industry, showing great potential. Utilizing them for food purposes might entail various direct and indirect economic benefits and positive environmental impacts, while reducing disposal costs and increasing the value of the coconut tree.
Purpose: Sri Lanka is a tropical island with prominent four monsoonal seasons. The rainfall pattern of the country follows a bi-modal distribution with two peaks centred on two growing seasons. Model the bimodel rainfall distribution of Sri Lanka using the Markov probability chain model was the objective of this study.
Research Method: Both first and second-order Markov probability models were developed for Anuradhapura, Badulla, Hambanthota and Katunayake using daily rainfall data for 1981–2011 period and the locations were selected to cover the major parts of the country. The model fit was done using Instat Statistical Programme.
Findings: Both first and second-order Markov models successfully described bi-modal distribution of rainfall. In general, both transitional probabilities in the first order (p_rd and p_rr) and three transitional probabilities of second-order except rainfall after dry day and rainy day (p_rrd) followed a bi-modal pattern with two peaks. The sum of the logs of the rainfall amount (lr) and the amount of rainfall on rainy days (r_mean) also showed two peaks for two growing seasons. In both models, stations in the dry zone showed higher agreement in the simulated rainfall.
Research Limitations: Lack of continuous long-term rainfall data is one of the major limitations.
Originality/ Value: It is evident that both first and second-order Markov chain probability models are very much capable to explain the bi-modal rainfall distribution in Sri Lanka.
Sri Lanka is affected by the El Niño phenomenon as well documented by multiple research studies. However, the El Niño influence is modulated by the sea-surface-temperature and atmospheric phenomenon in and above the Indian Ocean and other atmospheric processes. As an island with year-round rainfall, the influences vary by region and season. The impact of El Nino on water resources, agriculture, disaster risk and health are nuanced. Here, we document these nuances along with efforts to manage the risks and opportunities through communication of predictions of the major El Nino events in recent decades.KeywordsEl ninoClimate variabilityIndian oceanSri Lanka
Citation: Chen, X.; Feng, L.; Yao, R.; Wu, X.; Sun, J.; Gong, W. Prediction of Maize Yield at the City Level in China Using Multi-Source Data. Remote Sens. Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons At-tribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Abstract: Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.
Annual forecasts for the Australian macadamia crop have been issued since 2001, with varying (and not always improving) degrees of accuracy. Regression models using climate variables have formed the basis for these forecasts, with general linear model (GLM) ensembles being adopted more recently. This has proven to be a challenging task, as there are only a small number of observations (18) combined with a large number (90+) of independent variables – these being different climate measures for different times of the year (representing ‘key physiological periods for macadamia trees’). Also, these ‘assumedly-independent’ variables contain various degrees of correlation. This study uses cross-validation, with the most recent data for the two dominant production regions of Australia (Lismore and Bundaberg), to investigate the relative performance of alternate modelling methods. These modelling methods were GLMs, partial least squares (PLS) regression and LASSO (least absolute shrinkage and selection operator) penalised regression. Model ensembles, which have been shown to be beneficial in many alternate disciplines, are used to advantage. Both GLMs and PLS produced quite-disappointing results, failing to meet the project's benchmarked accuracy of ±10% error. The optimal LASSO models performed notably better, with a further improvement when ensembles were incorporated. The lowest mean absolute error (MAE) rates here were 9.0% for Lismore and 5.9% for Bundaberg. Hence LASSO ensembles will be adopted for future forecasts of the Australian macadamia crop.
Horticulture sector plays a prominent role in economic growth for most of the developing countries. India is the largest producer of fruits and vegetables in the world next only to China. Among the horticultural crops, fruit crops are cultivated in majority of the area. Fruit crops play a significant role in the economic development, nutritional security, employment generation, and overall growth of a country. Among fruit crops, mango and banana are largest producing fruits of India. Generally, Karnataka is called as the horticultural state of India. In Karnataka, mango and banana are highest producing fruit crops. With these prospective, yield of mango and banana of Karnataka have been chosen as study variables. Forecasting is a primary aspect of developing economy so that proper planning can be undertaken for sustainable growth of the country. In this study, classes of linear and nonlinear, parametric and non-parametric statistical models have been employed to forecast yield of mango and banana of Karnataka. The major drawback of linear models is the presumed linear form of the model. In most of the cases, the time series are not purely linear or nonlinear as they contain both linear and nonlinear components. To overcome this problem a hybrid model has been proposed which consists of linear and nonlinear models. The hybrid model with the combination of Autoregressive Integrated Moving Average (ARIMA) and Support Vector Regression model performed better in both model building as well as in model validation as compared to other models.
This study titled, “Climate Risks in the Region: ways to comprehensively address the related social, economic and environmental challenges”, was commissioned by the SAARC (South Asian Association for Regional Cooperation) Secretariat following the Thimphu Statement on Climate Change adopted by the Heads of State or Government of Member States of SAARC at the Sixteenth SAARC Summit (Thimphu, 28-29 April 2010).
Based on the observations recorded for four years, at Kasaragod and Kayangulam, Kerala, India, on palms of ordinary tall variety, growing under rainfed conditions, appropriate prediction equations have been proposed to estimate the annual yield of coconuts, at selected periods of the year, based on a total count of nuts in different stages of maturity.
The influence of El Niño-Southern Oscillation (ENSO) on crop production in the southeastern US was studied to identify crops that are vulnerable to ENSO-related weather variability and therefore likely to benefit from application of ENSO-based climate forecasts. The historical (1960-1995) response of total value and its components (yield, area harvested and price) to ENSO phases and quarterly sea surface temperature anomalies (SST) in the eastern equatorial Pacific was analyzed for six crops (peanut, tomato, cotton, tobacco, corn and soybean) in four states (Alabama, Florida, Georgia and South Carolina). ENSO phase significantly influenced corn and tobacco yields, the areas of soybean and cotton harvested, and the values of corn, soybean, peanut and tobacco. ENSO phases explained an average shift of $212 million, or 25.9%, of the value of corn. Canonical correlation analysis (CCA) identified significant responses of corn, soybean and cotton yields, and peanut value to SST across the region; and of peanut and tobacco yields, and tomato and soybean values in particular states.
The influence of El Niño–Southern Oscillation (ENSO) on crop production in the southeastern United States was studied to identify crops that are vulnerable to ENSO-related weather variability and therefore likely to benefit from application of ENSO-based climate forecasts. The historical (1960–95) response of total value and its components (yield, area harvested, and price) to ENSO phases and quarterly sea surface temperature anomalies (SST) in the eastern equatorial Pacific was analyzed for six crops (peanut, tomato, cotton, tobacco, corn, and soybean) in four states (Alabama, Florida, Georgia, and South Carolina). ENSO phase significantly influenced corn and tobacco yields, the areas of soybean and cotton harvested, and the values of corn, soybean, peanut, and tobacco. ENSO phases explained an average shift of $212 million or 25.9% of the value of corn. Canonical correlation analysis identified significant responses of corn, soybean, and cotton yields, and peanut value to SST across the region; and of peanut and tobacco yields, and tomato and soybean values in particular states.
1] Investigating the September to December rainy season in Sri Lanka associated with the Maha rice growing season provides insights into the Asian monsoon during the boreal fall. Here, the modulation of the Maha rainfall by the tropical air_sea coupled phenomenon referred to as the Indian Ocean Dipole (IOD) is documented. The Maha rainfall has a strong and robust association with the IOD from 1869 to 2000. The anomalously warm sea surface in the western Indian Ocean associated with the positive IOD phase induces large scale convergence in the lower troposphere extending to Sri Lanka leading to the preponderant enhancement of Maha rainfall.
Recently, it was reported that the relationship of the Indian southwest monsoon rainfall with El Niño- Southern Oscillation (ENSO) has weakened since around 1980. Here, it is reported that in contrast, the relationship between ENSO and the northeast monsoon (NEM) in south peninsular India and Sri Lanka from October to December has not weakened. The mean circulation associated with ENSO over this region during October to December does not show the weakening evident in the summer and indeed is modestly intensified so as to augment convection. The intensification of the ENSO-NEM rainfall relationship is modest and within the historical record but stands in contrast to the weakening relationship in summer. The intensification of the circulation is consistent with the warming of surface temperatures over the tropical Indian Ocean in recent decades. There is modestly intensified convection over the Indian Ocean, strength- ening of the circulation associated with ENSO (Walker circulation), and enhanced rainfall during El Niño episodes in a manner consistent with an augmented ENSO-NEM relationship.
Studies on the effect of climatic factors such as rainfall, relative humidity, temperature, sunshine hours, pan evaporation, evapotranspiration, solar radiation, vapour pressure and wind velocity on the button shedding, premature nut fall, and final nut yield of coconut, and crop-weather models developed to predict nut yield, are reviewed. The type of data and statistical analyses techniques used and the areas of research poorly addressed under the different topics are highlighted.
Efectos de las condiciones climáticas y el tiempo en el coco
The coconut yield is harvested in six picks per year at two-monthly intervals. The yield variation between and within years is very complex and this variability has not yet been explained. The analysis of long-term nut yield and monthly climate data: rainfall (RF), pan evaporation (EV), sunshine duration (SS), wind velocity (WV), minimum and maximum air temperatures (TMIN and TMAX), and relative humidity in forenoons and afternoons (RHAM and RHPM), using multivariate methods enabled the use of the variables TMAX, RHPM and EV as significantly important determinants (parsimonious set of variables) to represent the effects of climate on coconut irrespective of picks. Parsimonious models developed using these three variables explain how the development of bunches during the active growth period responded to climate variables without physiological parameters. The models are desirable where interpretation is concerned. The yields of picks one to six were determined by the climate variability during February, June, July, September, December and February respectively. Based on the models the proper timing of the use of some agronomic practices to enhance the productivity was recommended. A common model was also fitted (R2 = 0.81; p < 0.002) to estimate the annual yield 18 months in advance using EV, RHPM and TMAX. The three variables influence the microclimate around the crown of the palm for utilizing solar radiation in dry matter partitioning and thereby nut production. The method used to screen climatic variables so as to develop parsimonious crop–weather models using multivariate and univariate techniques can be used for other tree crops.
As part of an effort to demonstrate the use of climate predictions for water resources management, the El Niño/Southern Oscillation (ENSO) influences on stream flow in the Kelani River in Sri Lanka were investigated using correlation analysis, composite analysis and contingency tables. El Niño (warm phase of ENSO) was associated with decreased annual stream flow and La Niña (cold phase of ENSO) with increased annual flows. The annual stream flow had a negative correlation with the simultaneous ENSO index of NINO3·4 that was significant at the 95% level. This negative correlation is enhanced to a 99% level if the aggregate January to September or the April to September stream flow alone were considered. Although, there is little correlation between ENSO indices and stream flow during the October to December period, there is a high correlation between rainfall and NINO3·4 (r = 0·51, significant at the 99% level). Therefore ENSO based rainfall predictions can be used along with a hydrological model to predict the October to December stream flow. This study demonstrates the viability of using ENSO based predictors for January to September or April to September stream flow predictions in the Kelani River. The October to December stream flow may be predicted by exploiting the strong relationship between ENSO and rainfall during that period. Copyright © 2003 John Wiley & Sons, Ltd.
Despite advances in the capacity to predict the evolution of the El Niño–southern oscillation (ENSO) phenomenon and advances in understanding the influence of ENSO on rainfall in tropical regions such as Sri Lanka, there has been limited use of climate predictions for agricultural decision-making. Climatic fluctuations have a profound influence on the cultivation of crops such as rice, which is the staple food in Sri Lanka. Here, the relationship between the sea-surface temperature-based ENSO index of NINO3.4, rainfall and the departure of Sri Lankan rice production from long-term trends, is analysed for the ‘Maha’ (October to March) and ‘Yala’ (April to September) cultivation seasons between 1952 and 1997.
During the El Niño phase, the Maha rice production frequently increased (10 out of 15 seasons) and the Yala production frequently decreased (10 out of 14 seasons). Conversely, during the La Niña phase, the Maha production decreased (seven out of ten seasons) and Yala production increased (six out of eight seasons). Floods, state interventions, civil disturbances, fertilizer price hikes and extreme anomalies in the previous season were noted in the majority of seasons in which these ENSO–production linkages were violated.
The correlation of the Maha rice production anomaly with the average NINO3.4 from October to December was significant at the 5% level and that with the aggregate October to December rainfall was significant at the 1% level. Yala rice production showed a significant relationship with concurrent NINO3.4 and a strong correlation (r = 0.60) with the previous season's rainfall. Yala cultivation is water constrained, and carryover storage from the previous season is often used to determine the extent of planting.
The relationships between ENSO and seasonal rice production and the relationship between Yala rice production and previous Maha rainfall could be used for agricultural management and policy formulation. Copyright
This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described. Copyright © 2001 Royal Meteorological Society
Advances in our ability to predict climate fluctuations months in advance suggest opportunity to improve management of climatic risk in agriculture, but only if particular conditions are in place. This paper outlines prerequisites to beneficial forecast use; highlights key issues, approaches and challenges related to each; and suggests an evolutionary strategy. The first prerequisite is that forecast information must address a need that is both real and perceived. Second, benefit arises only through viable decision options that are sensitive to forecast information. Third, benefit depends on prediction of the components of climate variability that are relevant to viable decisions. Fourth, appropriate forecast use requires effective communication of relevant information. Finally, sustained use requires institutional commitment and favorable policies. It is useful to consider three phases of effort: an exploratory phase to gain understanding and assess potential, a pilot phase characterized by co-learning between researchers and target decision makers, and an operational phase focusing on engaging and equipping relevant institutions. Although examples of use and potential use, and advances in institutional support, are cause for optimism, use of climate prediction by agriculture s still too new to support strong generalizations about its value.
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