Production landscapes depend on, but also affect, ecosystem services. In the Rejoso watershed (East Java, Indonesia), uncontrolled groundwater use for paddies reduces flow of lowland pressure-driven artesian springs that supply drinking water to urban stakeholders. Analysis of the water balance suggested that the decline by about 30% in spring discharge in the past decades is attributed for 47 and 53%, respectively, to upland degradation and lowland groundwater abstraction. Consequently, current spring restoration efforts support upland agroforestry development while aiming to reduce lowland groundwater wasting. To clarify spatial and social targeting of lowland interventions five clusters (replicable patterns) of lowland paddy farming were distinguished from spatial data on, among other factors, reliance on river versus artesian wells delivering groundwater, use of crop rotation, rice yield, fertiliser rates and intensity of rodent control. A survey of farming households (461 respondents), complemented and verified through in-depth interviews and group discussions, identified opportunities for interventions and associated risks. Changes in artesian well design, allowing outflow control, can support water-saving, sustainable paddy cultivation methods. With rodents as a major yield-reducing factor, solutions likely depend on more synchronized planting calendars and thus on collective action for effectiveness at scale. Interventions based on this design are currently tested.

Carbon stock estimates are very important to support carbon policies at the regional level and sustainable environmental management. Rubber plantation is one of the carbon-absorbing ecosystems, due to its long life and large biomass content. The aim of this study was to estimate the above-ground carbon stock based on Sentinel 2A remotely sensed imagery, through vegetation index approaches. In the initial stage, the image was corrected radiometrically to obtain a bottom of atmosphere (BoA) reflectance values, so that all spectral indices that were run could provide reliable results. The vegetation indices used in this study were RVI (Ratio Vegetation Index), NDVI (Normalised Difference Vegetation Index), ARVI (Atmospheric Resistant Vegetation Index), and SARVI (Soil and Atmospherically Resistant Vegetation Index). The values generated from those indices were correlated with field data of carbon stock, which was derived from breast height diameter (BHD)-based biomass measurements and allometric equations. Correlation and regression analyses of carbon stock and vegetation indices were then used to interpolate the samples to the entire study area, using exponential, logarithmic, and quadratic equations. The resultant above ground carbon stock maps were then tested for accuracy assessment using field data collected independently. It was found that the ARVI-based estimation model with BoA reflectance radiometric correction, combined with exponential regression equation, showed the best accuracy values of 84.48% (supported by r2 = 0.473). Based on this model, the above-ground carbon stock estimate in Ngobo and Getas Plantation, PTPN IX were 527,072.39 tons in an area of 2,656,615 hectares, or 198.4 tons/hectares.

No abstract