PERBAIKAN SIFAT FISIKA DAN KIMIA TANAH PADA LAHAN BEKAS TAMBANG INTAN DI CEMPAKA DENGAN METODE COMPOSTING BERBAHAN DASAR SAMPAH ORGANIK PASAR

Penelitian ini bertujuan untuk mengidentifikasi kondisi suhu, pH dan kelembapan pada bioremediasi metode composting, mengidentifikasi perubahan sifat fisika dan kimia tanah bekas tambang intan sebelum dan sesudah proses composting juga mengidentifikasi komposisi terbaik pada composting berbahan dasar sampah organik pasar sistem open windrow. Rancangan penelitian menggunakan Rancangan Acak Lengkap (RAL) dengan 4 perlakuan dan 3 ulangan. Perlakuan yang dimaksud yaitu A (100% tanah) sebagai kontrol, B (25% tanah : 75% kompos), C (50% tanah : 50% kompos), D (75% tanah : 25% kompos). Hasil penelitian suhu, pH dan kelembapan keempat perlakuan selama proses bioremediasi dengan metode composting mengalami fluktuasi. Berdasarkan hasil uji T terdapat perbedaan nilai C-organik, N-total, Rasio C/N, KTK serta warna sebelum dan sesudah proses composting. Hasil Uji LSD 5% variasi komposisi terbaik yang mampu memperbaiki kualitas tanah tambang adalah variasi komposisi 25% tanah : 75% kompos. Kata kunci: composting, C-organik, N-total, KTK, warna tanah. The purpose of this research is to identify the conditions of temperature, pH and moisture in composting method of bioremediation, identify changes in physical and chemical properties of diamonds before and after the composting process and also identify the best composition on composting made from market organic waste of the open windrow system. The research design used a Completely Randomized Design (CRD) with 4 treatments and 3 replications. The treatment referred to is A (100% soil) as control, B (25% soil: 75% compost), C (50% soil: 50% compost), D (75% soil: 25% compost) with the weight of each one treatment / 40 kg stack and 30 cm stack height. The results of the research of temperature, pH and moisture of the four treatments during the bioremediation process by composting method are fluctuated. Based on the results of the T test there are differences in the value of C-organic, N-total, C / N ratio, CEC and soil color before and after the composting process. LSD 5% test results of the best composition variations that can improve the quality of mine soil is a variation of the composition of 25% soil: 75% compost. Keywords: CEC, composting, organic C, soil color, total N.

SIFAT FISIK GAMBUT PEDALAMAN PADA LABORATORIUM ALAM HUTAN GAMBUT SEBANGAU, KALIMANTAN TENGAHThe Physical Properties Of Inland Peat In Natural Laboratory Of Peat Swamp Forest (LAHG) Sebangau, Central Kalimantan

Indonesia has the largest peat area in the tropical zone, which estimated about 21 million ha, with a percentage of 70% of the peat area in Southeast Asia and 50% of the world's tropical peatlands. This study aims to evaluate the physical properties of soil in each layer of soil and land cover in inland peat swamp forests in LAHG in Central Kalimantan. The research used the profile method (minipit) measuring 120 cm x 120 cm. Each location is given 3 plots on 2 (two) land cover namely forest and burnt area. The research location is in LAHG. This research was conducted in July-December 2020. Samples were analyzed at the Banjarbaru Research and Development Laboratory. The parameters observed were bulk density, moisture content, fiber content, soil color, infiltration, and hydraulic conductivity. The research data were analyzed statistically by means of regression and correlation analysis. The results showed that the study of the physical properties of inland peat soil in the forest had bulk density ranging from 0.10 to 0.15 g / cm-3, moisture content 541.18-910.00%, fiber content 8-40%, infiltration -0 , 58-29.27 ml / hour, hydraulic conductivity 2.4-66.6 cm / hour with reddish black soil color. Whereas on burnt land, bulk density ranged from 0.10 to 0.15 g / cm-3, moisture content was 500.00-916.67%, fiber content was 12-52%, infiltration was -3.19-60.99 ml. / hr, hydraulic conductivity 2.4-30.6 cm / hr with dark black soil color. The study relationship pattern shows positive and negative.

PENGARUH BIOCHAR SEKAM PADI TERHADAP SIFAT FISIK TANAH DAN HASIL TANAMAN JAGUNG (Zea mays) DI KELURAHAN LAPE KECAMATAN AESESA

This study aims to determine the condition of the physical properties of the soil and maize yields given rice husk biochar in Lape Village, Aesesa District. The continuous use of inorganic fertilizers will have a negative impact on the soil such as destroying the physical properties of the soil and reducing maize yields. Therefore, this research is a descriptive study. Determination of soil samples using purposive sampling method with the criteria of land cultivation of corn and rice husk biochar. The variables observed were soil color, structure, texture, porosity, bulk density, moisture content, temperature, humidity and maize yield. The results showed that the criteria for the physical properties of the soil in the maize cultivation area were dusty clay soil texture and categorized as S1 class, soil color was dominated by 10 YR 2/1 Black and categorized as S1 class, and soil structure was dominated by granular criteria. B0 content density value: 1.01; B2: 0.9; B3: 0.87; B4: 0.81; B4: 0.8 and categorized as S2 class, soil porosity value B0: 61.76; B1: 65.91; B2:67,18; B3: 69.41; B4; 69.89 and categorized as S1 class. Value of water content B0: 36.78; B1:41,14; B2: 40.82; B3: 35.06; B4: 39.55 and categorized into class N. temperature analysis results B0: 33.75; B1: 34.00; B2: 32.75; B3: 33.25; B4: 34.00 and can be categorized as class N. Moisture analysis results B0: 67.00; B1: 68.00; B2: 64.50; B3: 66.25; 68.00 and categorized in S1 class. And maize yields. Corn kernels yield tan-1(g) B0: 347.30; B1: 360.40; B2: 380.70; B3: 385.40; B4: 401.35. Yield of shelled corn ha-1(tons) B0: 9.26; B1: 9.61; B2: 10,15; B3: 10.28; B4: 10.70

Evaluation of soils on a toposequence formed from the coastal plain sands of the Imo river basin, Southeastern Niger

Due to the increasing land degradation in southeastern Nigeria – with particular reference to the ubiquitous gully network in the region – selected soils formed from the Coastal Plain Sands of the Imo River Basin were evaluated and characterized to determine their potentials and capabilities. , Three profile pits were consequently dug on the upper slope (Umuariam), -mid-slope (Umulogho), and foot slope (Umungwa) of three towns on the Imo River Basin underlain by the coastal plain sands. The soil color ranged from greyish brown (5 YR 5/2) to dull brown (7.5 YR 5/3) in the topsoil and from dull orange (5 YR 6/4) to orange (5 YR 6/7) in the subsoil. The soils had sandy topsoil and relatively more clayey subsoil. The pH (in KCl) ranged from very strongly acidic (4.5 – 5.0) to moderately acidic (5.1 – 6.0). They had low organic matter, low total nitrogen, low ECEC, low Al saturation, and moderate base saturation. The soils of Umuariam (Upper Slope) and Umulogho (Mid Slope) were classified as Typic Hapludults (Chromic Acrisols), while that of Umungwa (Foot Slope) was classified as Typic Udipsamments (Rubic Arenosols). Umuariam had a USDA land capability class of IIes and a USBR land capability class of 2v/C. Umulogho and Umungwa both had a USDA and USBR capability class of IVs and 3v/C respectively. Though moderately to marginally irrigable, the soils can still produce increased and sustainable agricultural yield if the appropriate land use and husbandry practices are adopted, with particular reference to erosion control, organic manure, lime, and fertilizer application.

Adaptive scale weighted fuzzy C-Means clustering for the segmentation of purple soil color image

The segmentation and extraction of the purple soil region from purple soil color image can effectively avoid the influence of background on recognition of soil types. A scale weighted fuzzy c-means clustering algorithm(SWFCM) is proposed for effective segmentation of purple soil color image. The main work is to establish the maximum difference optimization model with the mean of Gaussian distance between each pixel and each peak of the image histogram, and optimize the clustering number and the initial clustering centers. Then, the dispersion of each class is defined to weight the Euclidean distance between the pixel and the clustering center and improve the optimization model of FCM for raising its clustering performance. Aiming at the problem of removing scattered small soil blocks in the background and filling holes in the purple soil region, the algorithm of extracting the boundary of the purple soil region and the algorithm of filling the purple soil region are proposed. Finally, the normal and robust experiments are carried out on the normal sample set and robust sample set. And the performances of relative algorithms are compared, which involves the previously released FCM algorithms and some methods for the segmentation of purple soil color image and our proposed algorithm. Experimental results show that performance of SWFCM is better and it can provide a high reference for adaptive segmentation of purple soil color images. Especially for robust experiment images, its average segmentation accuracy is improved by 6 . 64% ∼ 8 . 25 % compared with other purple soil segmentation algorithms.

Assessing the soil color by traditional method and a smartphone: a comparison

Based on the hypothesis that there is a high agreement between pedologists and a smartphone application in the assessment of soil color; the objective was to compare the perceptions of pedologists and an application in obtaining the color of an Argissolo [Lixisol] (A, E and B horizons). Ten aggregates of each horizon were collected. In a single day, under the same lighting conditions, three pedologists described the color components (hue, value, and chroma) of each aggregate (dry and moist soil) using the Munsell soil color chart. Each one of the ten aggregates, from each horizon, was photographed (dry and moist soil sequence) using the camera of a Motorola Moto G4 Plus smartphone. The distance of the camera to the aggregates was 25 ± 5 cm. Also, each aggregate was placed on a white sheet of A4 size paper (background). The application used was Soil Analysis Pro. The percentage of agreement between pedologists and application was obtained concerning hue, value, and chroma. The data were subjected to analysis of variance, in a completely randomized design, with ten replicates. Action Stat® software was used for statistical analysis. It was concluded that the agreement between pedologists and the smartphone application was medium for hue and chroma and low for value. For the dry soil condition, there is a high agreement between pedologists and the smartphone application, especially in the perception of hue and chroma. Thus, the smartphone application has the potential to be used in routine descriptions of soil color.

PERCEPTIONS ET STRATEGIES PAYSANNES DE GESTION DE LA FERTILITE DES SOLS DANS LA REGION DE TILLABERY DE LOUEST DU NIGER

Farms face declining soil fertility. Maintaining this fertility becomes a concern for famers who adopt several strategies to this end. Hence, it is important to know these strategies to better improve and value them. It is in this context that a study was carried out in 15 villages within three municipalities of Tillabery region in Niger. Its objective is to study farmers soil fertility management perceptions and strategies in the municipalities of Gotheye, Tagazar and Torodi. Data collection was carried out through individual surveys of 300 farm managers, with 20 per village, or 100 per municipality. The respondents are mainly illiterate (56%) with agriculture (82%) and house farming (88%) as main activities. They said the soils are poor. Their indicators are soil color (40%), good germination (24%), yield (23%), and soil poorness indicator species (13%). To overcome this, farmers use more manure which is managed sparingly. However, the fallow, neglected by the majority of farmers, lasts only 2 or 3 years. The promotion of integrated soil fertility management through ANR, the use of manures and degraded lands restoration techniques are necessary for the sustainability and improvement of production systems.

Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India).

<p>Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or “tanks”) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.</p><p>We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (“Black” and “Red” soils). A change of soil color from “Red” in February 2017 to “Black” in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from “Red” to “Black”, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.</p><p>The results showed that 1) the CART method allowed to classify the “Red” and “Black” soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.</p>

Is It Better to Be Objectively Wrong or Subjectively Right?

ABSTRACT For many years, archaeologists have relied on Munsell Soil Color Charts (MSCC) as tools for standardizing the recording of soil and sediment colors in the field and artifacts such as pottery in the lab. Users have identified multiple potential sources of discrepancy in results, such as differences in inter-operator perception, light source, or moisture content of samples. In recent years, researchers have developed inexpensive digital methods for color identification, but these typically cannot be done in real time. Now, a field-ready digital color-matching instrument is marketed to archaeologists as a replacement for MSCC, but the accuracy and overall suitability of this device for archaeological research has not been demonstrated. Through three separate field and laboratory trials, we found systematic mismatches in the results obtained via device, including variable accuracy against standardized MSCC chips, which should represent ideal samples. At the same time, the instrument was consistent in its readings. This leads us to question whether using the “subjective” human eye or the “objective” digital eye is preferable for data recording of color. We discuss how project goals and limitations should be considered when deciding which color-recording method to employ in field and laboratory settings, and we identify optimal procedures.