Nutrient management in coffee-based agroforestry systems plays a critical role in soil nitrogen (N) cycling, but has not been well documented. The objective of this study was to evaluate the effect of coffee canopy management and fertilization on soil N dynamics. This study used a randomized complete block design (2 × 3 × 2) with four replications. There were three factors: 1) coffee canopy management (T1: Pruned, T2: Unpruned), 2) fertilizer type (O: Organic, I: Inorganic; M: 50% Organic + 50% Inorganic), and 3) fertilizer dose (D1: low, D2: medium, D3: high). Soil N dynamic indicators (i.e., total N, ammonium (NH₄⁺), nitrate (NO₃⁻), net N-NH₄⁺, net N-NO₃⁻, soil microbial biomass N) were measured at two soil sampling depths (0–20 cm and 20–40 cm). Results showed that pruning increased soil total N and microbial biomass N (MBN) by 10–56% relative to unpruned coffee trees. In contrast, the unpruned coffee canopy had 15–345% higher NH₄⁺, NO₃⁻, net N-NH₄⁺, net N-NO₃⁻, and microbial biomass N concentration than pruned coffee. Mixed fertilizer application increased NO₃⁻ and net N-NH₄⁺ accumulation by 5–15% relative to inorganic and organic fertilizers. In addition, medium to high dose fertilization led to a 19–86% higher net N-NO₃⁻ concentration and microbial biomass N as compared to low dose fertilization. The treatment of no pruning and mixed fertilizer at low to medium doses was the optimal management strategy to maintain soil available N, while pruning combined with organic fertilizer has the potential to improve soil total N and MBN.

Pacitan Regency is a region in East Java Province with varied landforms and high disaster potential, including drought. The drought hazard in this region has not yet been determined. This study was conducted to analyze the potential of drought in Pacitan Regency in 2018 with the previous two decades (1998 and 2008) to predict future droughts. The study also focused on verifying how land-use changes impact drought potential. Mapping drought potential was based on the Ministry of Forestry method and was modified for this study. Drought potential was determined by scoring features and analyzing with a weighted overlay. Reference parameters and patterns of land-use change, as determined by Landsat 5, 7, and 8 satellite imagery, were analyzed. Then, the changing pattern was used to predict future 2030 land-use patterns using business as usual (BAU) analysis. For comparison, a land-use analysis was also done using the land capability class (LCC) and regional spatial plan (RSP). Data was validated using a confusion matrix. The accuracy of the drought estimation for Pacitan Regency was 75%. The results showed that the drought potential high and very-high level risk groups increased. The increase occurred due to changes in land use, specifically land management and plant species selection. Based on the results of the predicting BAU analysis, the level of potential of drought will increase by 2030. The regional spatial plan (RSP) and LCC analysis determined that, with no drought intervention, drought hazard in Pacitan Regency will increase.

The need for irrigation water is influenced by soil water content or more precisely by available water (pF 2.5 and pF 4.2). There is a need for technological breakthroughs in using Unmanned Aerial Vehicle (UAV) to identify water content quickly and broadly and accurately. The study was conducted in an area of ±18 hectares in the Sisim Sub Watershed in September 2019 at 09.00 a.m. Aerial photographs were taken at an altitude of 100 m with DJI Phantom Pro 3.0. The number of observation points was 75 points, where 15 points for validation were calculated based on the map scale. Photo processing was made using Agisoft. The Digital Elevation Model (DEMNAS) with 8.2 m resolution was used to compare the red, green and blue bands. The analysis used was Co-Kriging Geo Statistics Analysis, the compilation of algorithms based on the regression equation and ten index formulations. Validation was done by correlation continued with the regression or paired t-test if the parameter relationship was close. The available water measured in the field ranged from 5.16-48.28%. The results showed that the formulation of soil water content could be run on the Red, Green, and Blue bands, Intensity index, TGI index, ExGreen index and DEMNAS with a weak correlation (below 0.5), where TGI had the highest value (r=0.32). A test of t-pairing was not done because of a weak correlation. The highest estimation of pF 4.2 is DEMNAS (r=0.35), and pF 2.5 was on the TGI index (r=0.4).

Precise and detailed land mapping is essential for sustainable land management, environmental conservation, and regional planning, especially in complex and diverse landscapes. This study aims to present an innovative framework for the development of Land Mapping Units (LMUs) at a detailed scale (1:20,000), through the integration of Random Forest (RF) analysis and high-resolution remote sensing data. This study was conducted in the South Malang Plateau, Indonesia (the area characterized by karst, tectonic, volcanic, and alluvial landforms) from June to December 2024. As part of the methodology, the study utilized a combination of geospatial data, including geological maps, DEM-derived topographical indices, and remote sensing indices (Normalized Difference Soil Index/NDSI, Soil Adjusted Vegetation Index/SAVI, Normalized Difference Water Index/NDWI, Modified Soil Adjusted Vegetation Index/MSAVI). A total of 10,903 field observation points were analyzed, with 70% used for model training and 30% for validation. The results show that RF-based LMUs achieved R2 of 0.93 and Root Mean Square Error (RMSE) of 0.645, which is reliable to use. The LMUs provide a comprehensive understanding of landform-specific characteristics, including soil fertility linked to parent material, erosion sensitivity, and slope variability. These insights support applications in precision agriculture, disaster mitigation, and environmental planning. Moreover, the result can guide informed decision-making to prioritize sustainable land management that effectively prevents land degradation in the South Malang Plateau region, as stated in the Sustainable Development Goals (SDGs). The study demonstrates the potential of combining machine learning and remote sensing to refine spatial analysis and address the limitations of manual mapping methods. The proposed framework is scalable and adaptable to other diverse landscapes, making it a valuable tool for advancing sustainable land management in a rapidly changing world.

Watershed hydrological conditions can decrease due to changes in land use and inappropriate land management. One of the watershed functions is providing water availability for agricultural areas. Along with the increasing area of agricultural land, there is often the issue of water availability, which has implications for the low opportunity for plants to use it. Infiltration is the initial process of water entry into the soil, so the availability of water is greatly influenced by this process. Agricultural land in the Kalisari sub-watershed is dry land and is dominated by dry fields. The area of the Kalisari sub-watershed has an area of ±5,000 ha divided into 5 land uses, namely mahogany-coffee agroforestry, pine-coffee agroforestry, scrub, dry land, and paddy fields. Infiltration measurements were spread over 43 measurement points, with the observed parameters being texture, bulk density, porosity, permeability, aggregate stability, and soil organic matter. The infiltration rate for all land uses is very fast (>25 cm hour-1), ranging from 12.00 cm hour-1 to 74.37 cm hour-1. The infiltration rate for all land uses was not significantly different; this was in line with soil properties, which included texture, bulk density (0.61-1.02 g cm-3), porosity (51.02-68.06%), permeability (4.88–6.79 cm hour-1), aggregate stability (2.11–3.34 mm), and organic matter (1.61-4.06%). However, the infiltration rate at the study site had a significant relationship with clay (r = -0.77), sand (r = 0.64), silt (r = 0.52) and soil organic matter (r = 0.48).

Pine plantations on volcanic slopes in Indonesia are considered to be forests and are managed for wood production and slope protection. Logging practices followed by replanting may affect soil health. Existing agroforestry management contracts allow farmers to intercrop with vegetables in young plantations and grow fodder grasses in older ones. However, critical data on hydrological functions in such systems are scarce, while concerns over heavy rainfall and floods increase. We explored the relationships between soil cover, soil carbon, earthworms, soil porosity and infiltration rates in relation to slope class in second-rotation pine plantations around two years of age (intercropped) and at ten-year old pine-grass stages. Five slope classes (0%–8%, 8%–15%, 15%–25%, 25%–45%, and >45%) were compared with three measurement points each. Basic soil chemical and physical characteristics were measured for the 0–10, 10–20 and 20–30 cm layers. Remnant natural forest was available as a historical reference only on the steepest slope class. Organic soil carbon (C_Org) divided by a texture-based reference level was 1.12, 0.32 and 0.49 for natural forest, young and old agroforestry on very steep slopes, respectively. Within pine-based agroforestry relative decline with slope class (1–5) was pronounced in earthworms (biomass −3.46, population −4.18) and infiltration rates (−2.35) while bulk density increased (0.49); for soil carbon (C_Org), nitrogen, available phosphorus and exchangeable Mg effects in the −1.26 to −1.68 range indicated a loss of functional topsoil. Differences with age of the agroforestry systems were much smaller but included a decreasing earthworm population but an increase in mean earthworm weight and partial recovery of the C_Org/C_Ref ratio. Pine-based agroforestry on very steep soils had only 10%–14% of the earthworm biomass and 35% of the infiltration rate of reference natural forest. Understory vegetation biomass and litter layer necromass were more than five-fold higher in the natural forest. Across all samples a higher C_Org and higher earthworm biomass were associated with complementary positive changes in infiltration rates and soil porosity. Regression analysis suggests equal skill of tree cover, soil C_Org, porosity, aggregate stability and earthworms to predict infiltration rates while explanatory variables were strongly correlated. Management of the pine plantations may have to achieve a closer approximation of the conditions in natural forests to effectively protect upper watersheds.