Assessment of growing seasons characteristics in the Dry zone of Sri Lanka based on stochastic simulation of rainfall and soil water status

Rainfall and crop water demand are two major agro-climatic variables that determine the crop production in the Dry zone of Sri Lanka. The lack of long series of historical data of these variables often hinders the proper understanding of the agricultural potential of the region. The large random variability displayed by such variables means that they are best simulated by appropriate stochastic models and can be used to replace the existing short series of data. The main objectives of this thesis are to characterise the major growing seasons of the Dry zone, Yala and Maha, using extended temporal variability of rainfall and crop water demand through the stochastic simulation and to predict the characteristics of upcoming seasons using the simulated and historical data. The rainfall process was modelled using three Markovian models: the first-order discrete time Markov model, the second-order discrete time Markov model and the continuous time Markov model. Out of them, the first-order discrete time Markov model is the preferred model on the basis of its statistical performance and the practical ease. The crop water use was estimated using a single-layer water balance model which accounts evapotranspiration as a stochastic element. A weekly system model was developed that incorporated the first-order Markov rainfall model and the soil water balance model. It characterises the two major growing seasons of the Dry zone using five agro-climatic indices: mean rainfall, dependable rainfall (DRF), moisture availability index (MAI), ratio of actual to potential evapotranspiration (AET/PET) and crop water satisfaction index (CWSI). The simulated mean onset of the Yala and Maha seasons were the standard weeks 13 and 40, respectively. The mean end of the Yala season was the standard week 20 whereas the mean end of the Maha season could occur any time after the standard week 5 and it varied depending on the index used. The simulation also revealed that though the Maha season is ceased by late January, the soil moisture remains well above the 50% of available soil moisture during the inter-season dry month, February. According to the simulation, at least one out of every ten years the Yala season could experience a complete crop failure and the possibility of occurrence of such a catastrophic event during the Maha season is negligible. The onset time of the seasonal rains as a predictor of the seasonal characteristics of Yala or Maha season was not clearly evident in this simulation study though such links have been apparent in other monsoonal areas of the tropic. Nevertheless, cursory examination of observed rainfall data and the appearance of EI Nino conditions in the Pacific Ocean points towards a possible trend of seasonal rainfall in the Dry zone. A special case of spatial interpolation of rainfall data was examined assuming that the spatial continuity of two neighbouring locations are exponentially correlated. It was shown that the exponential spatial interpolation model is a good candidate to estimate the mean parameters of weekly rainfall in the Dry zone

Improving water productivity in moisture-limited rice-based cropping systems through incorporation of maize and mungbean: A modelling approach

Crop and water productivities of rice-based cropping systems and cropping patterns in the irrigated lowlands of Sri Lanka have not been researched to the degree warranted given their significance as critical food sources. In order to reduce this knowledge gap, we simulated the water requirement for rice, maize, and mungbean under rice-based cropping systems in the Dry Zone of Sri Lanka. We evaluated the best combinations of crops for minimum water usage while reaching higher crop and water productivities. We also assessed the risk of cultivating mungbean as the third season/sandwich crop (i.e. rice-mungbean-rice) in different regions in Sri Lanka. In the simulation modelling exercise, APSIMOryza (rice), APSIM-maize and APSIM-mungbean modules were parameterised and validated for varieties grown widely in Sri Lanka. Moreover, crop productivities and supplementary irrigation requirement were tested under two management scenarios i.e. Scenario 1: irrigate when plant available water content in soil fell below 25% of maximum, and Scenario 2: irrigate at 7-day intervals (current farmer practice). The parameterised, calibrated and validated model estimated the irrigation water requirement (number of pairs of observations (n) = 14, R2 > 0.9, RMSE = 66 mm season−1 ha−1), and grain yield of maize (n = 37, R2 > 0.95, RMSE = 353 kg ha−1) and mungbean (n = 26, R2 > 0.98, RMSE = 75 kg ha−1) with a strong fit in comparison with observed data, across years, cultivating seasons, regions, management conditions and varieties. Simulated water requirement during the cropping season reduced in the order of rice (1180–1520 mm) > maize and mungbean intercrop = maize sole crop (637–672 mm) > mungbean sole crop (345 mm). The water productivity of the system (crop yield per unit water) could be increased by over 65% when maize or mungbean extent was increased. The most efficient crop combinations to maximise net return were diversification of the land extent as (i) 50% to rice and 50% to mungbean sole crops, or (ii) 25%, 25% and 50% to rice, maize and mungbean sole crops, respectively. Under situations where water availability is inadequate for rice, land extent could be cultivated to 50% maize and 50% mungbean as sole crops to ensure the maximum net return per unit irrigation water (115 Sri Lankan Rupees ha−1mm−1). Regions with high rainfall during the preceding rice cultivating season are expected to have minimum risk when incorporating a third season mungbean crop. Moisture loss through evapotranspiration from the third season mungbean crop was similar to that of a fallowed site with weeds.Authors acknowledge the funding received from the AusAIDCSIRO project “Improved climate forecasting to enhance food security in Indian Ocean Rim countries” (AusAID Agreement 59553) through the Agriculture Education Unit(AEU) ofthe Faculty of Agriculture, University of Peradeniya to conduct the study, and the Department of Agriculture, Sri Lanka for providing the access to collect secondary data on crop performances and management, and weathe

Análise da transição entre dias secos e chuvosos por meio da cadeia de Markov de terceira ordem Analysis of the transition between dry and wet days through third-order Markov chains

O objetivo deste trabalho foi verificar se as ocorrências de dias secos e chuvosos são condicionalmente dependentes da seqüência dos três dias secos e chuvosos anteriores, numa zona pluviometricamente homogênea, por meio da cadeia não-homogênea de Markov de terceira ordem. Os resultados mostraram que as probabilidades diárias de transição podem ser adequadamente estimadas, com base em dados agregados bimestralmente, seguidas de interpolação por meio de funções sinusoidais. Além disso, evidenciou-se que, naquela zona, as ocorrências diárias de chuva são condicionalmente dependentes da seqüência de dias secos e chuvosos nos três dias anteriores. A cadeia não-homogênea de Markov de terceira ordem é um importante instrumento para a análise da dependência entre as seqüências de dias secos e chuvosos em determinadas regiões.<br>The aim of this work was to verify if the occurrence of dry and wet days are conditionally dependent on the sequences of the dry and wet three preceding days, in a rainfall homogeneous area, using the nonhomogeneous third-order Markov chains. The results showed that daily transition probabilities can be properly estimated from two-month aggregate data, and then adjusted by means of sinusoidal functions. Besides, it was evidenced that everyday rain events in that area are conditionally dependent on the sequences of the dry and wet three days previous to occurrences. The third-order nonhomogeneous Markov chains are an important instrument for the analysis of the dependence between sequences of dry and wet days in certain areas