Integrated weed control strategies are essential for organic and integrated nutrient management, where both systems are progressing with a fundamental of zero or minimum synthetic chemical cultivations. For optimizing the outcome of weed management, a better understanding of the weed dynamic is needed. Especially, with the absence of herbicides, weeds are expected to be controlled by the system itself, during the transition period under rice-based crop rotation systems. This study was conducted to estimate the weed abundance, growth, and composition during the transitional period with conventional (CONV), integrated (INT), and organic (ORG) nutrient management under four crop diversification intensities in a dry zone of Sri Lanka. Monocrop rice and a rice-maize rotation were the starting point. After 1 year, the diversification intensity was increased by adding interseason sunnhemp (rice-sunnhemp-rice and rice-sunnhemp-maize). Weed density and weed biomass were measured at 20 DAS and 60 DAS intervals. Weed density was higher in ORG during the early growth stages of monocrop rice rotation in the 1^st cycle, and monocrop rice and rice-sunnhemp-rice rotation in the 2^nd cycle while didn’t show any changes during the later growth stage of all systems in both cycles. The total weed biomass in ORG increased with increasing crop diversification. Overall, crop rotation in INT reported the lowest weed density and biomass after two cycles. In the CONV with rice-sunnhemp-maize rotation, weed biomass had declined, while in ORG grass biomass decreased only in sunnhemp cultivated rotations. Overall, INT was the best for weed suppression irrespective of crop rotation intensities. Monoculture with rice in the INT was able to suppress weed more effectively than rice-maize rotation.

This study provides an in-game prediction of the winner for Twenty20 (T20) cricket by focusing on the matches played in the Indian Premier League. For the analysis, data were collected from 812 completed matches played between 2008 and 2020. Initially, several candidate features were identified, and then the LASSO method was applied as a feature selection technique to identify the most important set of features. Based on the identified important features, predictions are provided for each stage of a match where a T20 match can consist of a maximum of 240 stages. For each stage, three classification models were formed using Naive Bayes, Logistic Regression and Support Vector Machines. The prediction accuracy was used for evaluating the findings of the study, and the prediction accuracy of each model indicates the ratio between the number of correctly predicted instances and the total number of predicted instances. Naive Bayes demonstrated prediction accuracies ranging from 53.08% to 91.76% between the first and the 240th stage of matches, whereas the accuracy of Logistic Regression varied from 56.92% to 97.65%. In comparison, Support Vector Machines also displayed comparable outcomes with a prediction accuracy of 55.00% at the first stage, and 90.59% at the 240th stage. Furthermore, a strategy generator that assists the competing teams in the second innings to devise winning strategies, is presented in this study alongside an interactive web-based application for making in-game predictions, and for assisting the end users (players and coaching/managing staff) in decision making, based on the generated winning strategies.

Offsetting carbon footprints by sequestering carbon through plant biomasses has become a key concern under modern thinking on climate change mitigation. This study aimed to estimate the carbon sequestration capacities of coconut-based gliricidia mixed cropping systems in Sri Lanka and, importantly, to develop an allometric model for non-destructive estimation of carbon. Five major coconut age groups and four major gliricidia age groups were selected to estimate the total carbon stock. The age groups of coconut considered were 10, 20, 30, 40, and 50 years, with corresponding carbon stocks of 22.59, 34.99, 53.13, 63.40, and 66.03 Mg[C]ha-1, respectively. In gliricidia stands, carbon stocks were 25.53, 46.16, 83.83, and 106.09 Mg[C]ha-1, respectively, for age groups of 5, 10, 15, and 20 years. It is recommended that gliricidia be introduced as an intercrop in coconut plantations 20 years after the establishment of the latter. For this study, a 30-year-old coconut plantation with a 10-year-old gliricidia intercrop, along with its associated ground cover vegetation and soil carbon stock, were considered the benchmark agroforestry system. In the 30-year-old coconut monocropping system, the total carbon stock was 67.76 Mg[C]ha-1, which consisted of uniform coconut, 1.24 Mg[C]ha-1 from ground cover vegetation and 13.39 Mg[C]ha-1 from the soil carbon stock at a depth of 30 cm. The overall carbon sequestration rate for this monocropping system was 2.25 Mg[C]ha-1yr-1 and abated CO2 in 8.23 Mg[CO2]ha-1yr-1. In the 30-year-old coconut and 10-year-old gliricidia mixed cropping system, the total carbon stock was 114.83 Mg[C]ha-1, which consisted of uniform coconut and Gliricidia, 1.05 Mg[C]ha-1 from ground cover plants, and 14.49 Mg[C]ha-1 from soil carbon stock at a depth of 30 cm. Compared to the coconut monocropping system, the coconut-gliricidia mixed cropping system had a higher carbon sequestration rate of 6.84 Mg[C]ha-1yr-1 and abated CO2 in 25.03 Mg[CO2]ha-1yr-1, which are around three times higher than the monoculture system.