The brown planthopper Nilaparvata lugens (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation. In this study, artificial neural network (ANN), random forest (RF) and classic linear multiple regression (MLR) analyses were applied and compared to forecast the BPH population using weather and host-plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand. Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density. An ANN model with integrated ground-based meteorological variables and satellite-derived host plant variables was more accurate for short-term forecasting of the peak abundance of BPH when compared with RF and MLR, according to a reasonably validating dataset (RMSE of natural log-transformed (ln) BPH light trap catches = 1.686, 1.737, and 2.015, respectively). This finding indicates that the utilization of ground meteorological observations, satellite-derived NDVI time series, and ANN have the potential to predict BPH population density in support of integrated pest management programs. We expect the results from this study can be applied in conjunction with the satellite-based rice monitoring system developed by the Geo-Informatic and Space Technology Development Agency of Thailand (GISTDA; http://rice.gistda.or.th) to support an effective pest early warning system.
<p>Coffee husk and coffee pulp are by-product of coffee fruit and bean processing, can be considered as potential functional ingredients for food production as coffee cherry flour (CCF). The CCF contains a lot of carbohydrates, proteins, caffeine, tannins, and polyphenols. In this study, CCF was combined with modified arrowroot starch (MAS) and modified cassava flour (MOCAF) into cookies and improvement on the physical, chemical, and nutraceutical properties of the cookies were studied. The cookies consisted of 20 % of MOCAF and 80 % mixed of modified arrowroot starch and CCF in five levels (80 %:0 %; 75 %:5 %; 70 %:10 %; 65 %:15 %; 60 %:20 %) and objective physical, chemical, and nutraceutical properties of the cookies were assessed. The results showed that the total dietary fiber content was enhanced from 11.69 % to 19.48 % with a high proportion of 20 % CCF. The cookies added with CCF displayed enhanced antioxidant activity. Acceptable cookies were obtained by adding 5 % CCF. Thus, the results implied that cookies with CCF addition obtained dietary fiber enriched cookies with improved antioxidant activity.</p>
This research identified and investigated the factors influencing the adoption of good agricultural practices (GAP) and the decision making of small-scale asparagus and sweet corn farmers in Thailand to produce for export. In the study, a total of 147 vegetable farming households (66 and 81 asparagus and sweet corn growers, respectively) were randomly selected from areas with intensive vegetable cultivation. The binary logistic regression was used to analyze the information collected from this survey. The results revealed that the income variable is the most influential factor in the GAP adoption by participating vegetable farmers and that the location factor exerts the most influence over the growers' export decision. Also, it is felt that to effectively increase the GAP adoption rate among the Thai vegetable growers, the exporters and relevant government agencies could make GAP certification compulsory.
This study analyzes 24 climate extreme indices over North Thailand using observed data for daily maximum and minimum temperatures and total daily rainfall for the 1960-2010 period, and HadCM3 Global Climate Model (GCM) and PRECIS Regional Climate Model simulated data for the 1960-2100 period. A statistical downscaling tool is employed to downscale GCM outputs. Variations in and trends of historical and future climates are identified using the nonparametric Mann-Kendall trend test and Sen's slope. Temperature extreme indices showed a significant rising trend during the observed period and are expected to increase significantly with an increase in summer days and tropical nights in the future. A notable decline in the number of cool days and nights is also expected in the study area while the number of warm days and nights is expected to increase. There was an insignificant decrease in total annual rainfall, number of days with rainfall more than 10 and 20 mm. However, the annual rainfall is projected to increase by 9.65% in the future 2011-2099 period compared to the observed 1960-2010 period.
This study was multiplicated by Arbuscular Mycorrhizal Fungi (AMF) indigenous in corn with pots culture at the greenhouse. The research will be conducted from August 2019 to October 2019 in Greenhouse, Laboratory of Microbiology, Mataram University, Indonesia. This research aims to determine the influence of AMF in dry land and the application of fertilizer concentration. This research was conducted isolate exploration in four villages at the Pujut Central district, Lombok, Indonesia i.e. Mertak, Sukadana, Kuta, and Sengkol Village. This research is an experimental study with a completely randomized factorial design with two factors i.e the AMF isolate type and the concentration of Johnson’s nutrient solution. The first factor with the level without AMF Isolates, Isolate 1, Isolate 2, and Isolate 3. While the second factor is the Johnson nutrient concentration i.e 50 % and 75 % solution. The results showed that were differences in growth such as crop height and the number of leaves where Isolate 1, gave the highest growth and number of leaves. The identification was obtained the Isolate 1 showed highest spore’s density and root infections is Isolate 1 with a spherical shape.
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