Mapping Soil Productivity Index on Plantation Land in ATP Jatikerto, Kromengan District, Malang Regency

It is feared that the decline in productivity of plantation crops in Malang Regency, so it is necessary to analyze the soil productivity index at ATP Jatikerto as one of the locations for producing plantation plants. This soil productivity index assessment was presented in the form of a map to make it easier to see the distribution pattern of soil productivity in ATP Jatikerto. The results of the calculation of the productivity index showed that the land potential varied from land with poor to sufficient criteria, namely maize land with an IP (Productivity Index) of 43.27, which is included in the bad IP criteria. In coffee, cocoa, sugarcane, papaya, and citrus fields, they are categorized as moderate with IPs of 50.14, respectively; 45.82; 39.45; 52.26; and 45.82. Actions that can be taken to overcome the problem of decreasing productivity are to carry out regular fertilization, both organic and inorganic, to keep nutrients available for cultivated plants, as well as adding organic matter using litter which not only serves to add nutrients but can also be used as organic mulch to prevent raindrops from falling directly to the ground so that the loss of topsoil can be minimized and increase the population of soil microorganisms. In addition, it can also use bagasse, blotong or manure which can improve the physical condition of the soil by reducing soil density and increasing macropores for better root growth, and ultimately increasing sugarcane yield.

Observing Sucrose Accumulation With Sentinel-1 Backscatter

In this study the impact of sucrose accumulation in Sentinel-1 backscatter observations is presented and compared to Planet optical observations. Sugarcane yield data from a sugarcane plantation in Xinavane, Mozambique are used for this study. The database contains sugarcane yield of 387 fields over two seasons (2018-2019 and 2019-2020). The relation between sugarcane yield and Sentinel-1 VV and VH backscatter observation is analyzed by using the Normalized Difference Vegetation Index (NDVI) data as derived from Planet Scope optical imagery as a benchmark. The different satellite observations were compared over time to sugarcane yield to understand how the relation between the observations and yield evolves during the growing season. A negative correlation between yield and Cross Ratio (CR) from Sentinel-1 backscatter was found while a positive correlation between yield and Planet NDVI was observed. An additional modeling study on the dielectric properties of the crop revealed how the CR could be affected by sucrose accumulation during the growing season and supported the opposite correlations. The results shows CR contains information on sucrose content in the sugarcane plant. This sets a basis for further development of sucrose monitoring and prediction using a combination of radar and optical imagery.

Forecasting Sugarcane Yield of India based on rough set combination approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.