The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationally accepted FAO Penman–Monteith (FAO-56 PM) equation. However, this requires numerous observed data, including solar radiation, air temperature, relative humidity, and wind speed, which in most cases are unavailable, particularly in developing countries such as the Philippines. This study developed models based on Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs) for the estimation of daily ETo using different input combinations of meteorological data in Region IV-A, Philippines. The performance of machine learning models was compared with the different established alternative empirical models for ETo. The results show that the SVM and ELM models, with at least T_max, T_min, and R_s as inputs, provide the best daily ETo estimates. The accuracy of machine learning models was also found to be superior compared to the empirical models given with same input requirements. In general, SVM and ELM models showed similar modeling performance, although the former showed lower run time than the latter.

Drought is common under rainfed lowlands in Lao People’s Democratic Republic, and with the uncertain onset of rains during the wet season, delay in transplanting results in yield reduction. This study aims to explore ways to ameliorate the negative influence of delayed transplanting on rice crop. A field experiment was conducted for two wet seasons to investigate the effect of seedling age and seedling density on crop performance in terms of grain yield and water productivity. The experiment was laid out in a split–split plot design in four replicates, with seedling age as the main plot, seedling density as the subplot, and varieties as the sub-sub plot. In both years, there were significant seedling age and variety interactions on grain yield. Higher grain yields were observed with older seedlings having stronger tillering propensity. Seedling density did not affect grain yields in both years, but on grain yield components. Shorter duration variety received less supplemental irrigation than longer duration varieties. Late transplanting improved total water productivity but decreased irrigation water productivity due to harvesting delay. The total crop growth duration (from sowing to maturity) was prolonged with transplanting delay. However, the total stay of plants in the main field (from transplanting to maturity) was reduced by 3–5 d for every 10 d delay in transplanting. The results indicated that a good selection of varieties and increasing seedling density improve crop performance and water productivity with delayed transplanting.

The Silway River has historically failed to meet safe fecal coliform levels due to improper waste disposal. The river mouth is located in General Santos City, the tuna capital of the Philippines and a leading producer of hogs, cattle, and poultry. The buildup of contaminants due to direct discharge of waste from chicken farms and existing water quality conditions has led to higher fecal matter in the Silway River. While there were technical reports in the early 2000s about poultry farming, this is the first study where fecal coliform from poultry farming was detected in the Silway River using highly sensitive protocols like qPCR. This study characterized the effect of flow velocity and physicochemical water quality parameters on chicken fecal contamination. Gene markers such as Ckmito and ND5-CD were used to detect and quantify poultry manure contamination through microbial source tracking (MST) and environmental DNA (eDNA) profiling. The results of this study showed the presence of chicken fecal bacteria in all stations along the Silway River. The results revealed that normal levels of water quality parameters such as temperature, pH, and high TSS concentrations create favorable conditions for chicken fecal coliforms to thrive. Multiple regression analysis showed that flow velocity and DO significantly affect chicken fecal contamination. A lower cycle threshold (Ct) value indicated higher concentration of the marker ND5-CD, which means higher fecal contamination. It was found that there was an inverse relationship between the Ct value and both velocity (R² = 0.55, p = 0.01) and DO (R² = 0.98, p = 0.2), suggesting that low flow velocity and low DO can lead to higher fecal contamination. Findings of fecal contamination could negatively impact water resources, the health of nearby residents, and surrounding farms and industries, as well as the health and growth of fish.

A field water balance model for efficient irrigation water management of corn was developed using Excel VBA. The model consists of five sub-components or modules, namely, (1) a plant subcomponent, (2) an effective rainfall subcomponent, (3) an evapotranspiration subcomponent, (4) a soil water dynamics subcomponent for the modeling of water flow into and within the soil layers, and (5) an irrigation subcomponent for the estimation of the required amount and timing of irrigation. The model was calibrated and validated using observed data from field experiments and the results showed a reasonably good agreement between the observed and simulated soil moisture values (MAE = 5.76 mm to 12.00 mm, RMSE = 6.83 mm to 13.12 mm, NRMSE = 0.102 to 0.196, and NSE = 0.37 to 0.90). The simulations emphasized that a significant amount of water savings can be achieved when rainfall is properly accounted for in managing water in the field, and that the frequency of rainfall occurrences is as important as the magnitude of rainfall received by the crops. The wide-ranging user-friendliness and simplicity of the model developed in this study can pave the way to eliminating the barriers which cause farmers to resist advancements in their farming practices as the model can easily be used not only by researchers and scientists but also by farmers, especially those with basic knowledge of spreadsheets.