Determination of Groundwater Prices for Sustainable Irrigation in JembranaRegency Subak Babakan Yehkuning is relatively new in obtaining Groundwater IrrigationNetwork, with the code YKN-127 in 2014. This study aims to determine the costcomponents and water prices that reflect the value of water use in a sustainablemanner. The method used in this research is quantitative calculation. The total waterdischarge from YKN-127 for three planting seasons in September 2015 until August2016 consecutively was 83,647.01 m3. This amount was derived from the total life ofthe pump for 2,733 hours, with a discharge 8.5 liters/second. The commoditiescultivated in three planting seasons were rice for two times and secondary crop (cornand soybean). Since the availability of ground water has a limited amount, it isimportant to note that the amount of water use should be less than the availabilities.The concept of sustainable value in the use of water is consisted of the total cost ofsupply, opportunity costs, and externalities costs. The results showed that the waterprice reflecting the sustainable water use value for irrigation at YKN-127 was Rp3,933.91/m3, which consisted of a full supply cost 65.9% (Rp 2,592.42/m3), anopportunity cost 27.9% (Rp 1,099.43/m3), and depletion premium 6.2% (Rp242.06/m3). This result is almost three times the operational and maintenance coststhat farmers usually pay for Rp 1,633.99/m3. This price describes the value that mustbe paid as an effort to maintain the development of irrigated agriculture sector andground water resources in order to remain sustainable in Jembrana, Bali Province.

In response to the 2020 COVID-19 pandemic, policymakers worldwide adopted unprecedented measures to limit disease spread, with major repercussions on economic activities and the environment. Here we provide empirical evidence of the impact of a lockdown policy on satellite-measured agricultural land greenness in Badung, a highly populated regency of Bali, Indonesia. Using machine learning and satellite data, we estimate what the Enhanced Vegetation Index (EVI) of cropland would have been without a lockdown. Based on on this counterfactual, we estimate a significant increase in the EVI over agricultural land after the beginning of the lockdown period. The finding is robust to a placebo test. Based on evidence from official reports and international press outlets, we suggest that the observed increase in EVI might be caused by labour reallocation to agriculture from the tourism sector, hardly hit by the lockdown measures. Our results show that machine learning and satellite data can be effectively combined to estimate the effects of exogenous events on land productivity.

No abstract