Spatial Variability Of Soil Fertility Research Articles

Spatial variability of soil fertility under different land use Types in Assosa District, Western Ethiopia

Spatial variability of soil fertility under different land use Types in Assosa District, Western Ethiopia

Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model

Accurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting growth, such as climate variability, soil characteristics, and water deficit. Improvements in the availability and resolution of spatial and temporal data combined with advancements in machine learning algorithms provide new opportunities to improve model predictions. This study shows how interpretable machine learning (ML) can be used to independently predict site soil fertility rating (FR) and incorporate these results into the 3-PG forest process-based model to accurately predict plantation growth. Four ensemble decision tree machine learning models—random forest trees, extremely randomized trees, gradient boost, and XG boost—were trained and compared using spatial cross-validation across the study area. FR predictions were estimated in relation to the influencing soil type and terrain characteristics, and interpretable ML methods were used to show how input feature permutations may relate to the soil fertility predictions. The results show the explanatory variables are similar across the selected ML models, with the strongest influencing variables being water leaching index, site aspect, and the silt and sand soil texture properties. The extremely randomized tree models showed the overall best performance, with only a small variation in performance across the four ML models. The method was applied to Eucalyptus nitens plantations covering over 63,000 ha in north-west Tasmania, Australia. The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R2 stand volume = 0.96, RMSE = 23.1 m3 ha−1) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas.

Spatial variability of soil fertility under agroforestry system and native forest in eastern Amazonia, Brazil

The usage of spatial tools might be helpful in the optimization of decision-making regarding soil management, with technologies that assist in the interpretation of information related to soil fertility. Therefore, the present study evaluated the spatial variability of chemical attributes of the soil under an agroforestry system compared to a native forest in the municipality of Tomé-açu, Eastern Amazon, Brazil. Soil samples were performed at 36 points arranged in a 55 x 55 m grid. The soils were prepared and submitted to analysis in order to determine pH in H2O, exchangeable calcium, magnesium, potassium and aluminium, available phosphorus, potential acidity, organic matter, bases saturation and aluminium saturation. For each soil attribute, the spherical, gaussian and exponential models were adjusted. After the semivariograms fitting, data interpolation for assessment of spatial variability of the variables was performed through ordinary kriging. The spherical and gaussian models were the most efficient models in estimation of soil attributes spatial variability, in most cases. Most of variables presented a regular spatial variability in their respective kriging maps, with some exceptions. In general, the kriging maps can be used, and we can take them as logistical maps for management and intervention practices in order to improve the soil fertility in the study areas. The results principal components indicate the need for integrated management of soil chemical attributes, with localized application of acidity correctors, fertilizers and other types of incomes, using the spatial variability of these fertility variables.

Spatial Distribution of Nutrients in Research Farm, Department of Soil Science, CCS HAU Hisar, Haryana

Mapping the spatial variability of soil fertility by employing Geographic Information System (GIS) provides an elicit information for current and future use. To know the fertility status of the research farm, sixty four surface soil samples were collected to evaluate the spatial distribution of nutrients. The soils of the study area were sandy loam to sandy clay loam in texture and slightly alkaline to moderately alkaline (7.40-8.20) in reaction and non-saline (0.19-0.86 dS m-1) in nature. Organic carbon was recorded high to low and varied from 0.22-0.82 percent. The soils of the research farm were low in available nitrogen (112-161 kg ha-1), high to medium in available phosphorus (10-22 kg ha-1) and high to medium in available potassium (170-538 kg ha-1). The diethylenetriaminepentaacetic acid (DTPA) extractable micronutrients (Zinc, Copper and Manganese) status was found moderate to sufficient except Iron, which was found in moderate to deficient category (4.30-12.56 mg kg-1). Available nitrogen, phosphorus and potassium showed high variation as indicated by coefficient of variance values (142.58, 16.63 and 10380.04, respectively). Organic carbon (0.02), zinc (0.07), iron (6.28), copper (0.19) and manganese (2.25) showed small variation as indicated by low variance. Organic carbon was significantly correlated with nitrogen and potassium suggesting synergistic effect. All the soils were low in available nitrogen that’s why application of nitrogenous fertilizers is necessary to ameliorate nutrient deficiency and enhance crop production. The spatial variability maps of nutrients provide an insight of fertility status of the area and this will help in easy monitoring of precision fertilizer management.

Maize nutrient yield response and requirement in the maize belt of Nigeria

Absence of site-specific nutrient recommendation and high spatial variability of soil fertility are major factors affecting maize response to applied nutrients in Nigeria. In this study, we assessed maize response to applied nutrients and nutrient use efficiency in different management zones (MZs), for designing site-specific nutrient management recommendations for maize in the maize belt of Nigeria. The maize belt in Nigeria was earlier delineated into four MZsMZs (MZ1 to MZ4) based on soil properties. In the current study, data from two different trials, nutrient omission trials (N = 293) and fertilizer response trial (N = 705), conducted in the years 2015–2017, were extracted for MZ1 to MZ3; to analyze maize yield responses to application of N, P and K, and secondary and micro-nutrients. Maize yield response to K application was only positive in MZ1. Responses to N and P application were positive for all MZs. However, the magnitude of maize response to P varied between the MZs, indicating a differentiation in the degree to which P is limiting maize production in the study area. Average nitrogen requirement was higher for MZ3 (138 kg ha−1), than for MZ2 and MZ1 (121 and 83 kg ha−1, respectively). Average P requirement was higher for MZ3 (45 kg ha−1) than for the other zones. Potassium requirement was 26% and 28% higher in MZ2 and MZ3 compared with MZ1 (∼15 kg ha−1). The use of the specific nutrient rates for the MZs may reduce risks and uncertainties in crop production. The delineated MZs of the maize belt of Nigeria that incorporates spatial variability in soil fertility conditions are useful for nutrient management for larger areas.

Geostatistical methods applied to soil fertility zoning

In conventional agricultural production systems, soil management is generally carried out without considering the spatial variability of its properties. This situation generates not only soil degradation but also an increase in production costs associated with the management of this factor. The objective of this research was to evaluate, through geostatistical methods, the spatial variability of soil fertility in Botana Experimental Farm of Universidad de Nariño. Spatial variability maps were estimated using the ArcGIS 10 program, the Kriging interpolation method, and the optimal ranges of soil fertility for the Andean region as projection parameters for making decisions related to land use. The fertility zoning of the farm was established, classifying soil as having high, medium, and low fertility. Most of the experimental farm had low fertility soils (20.7ha), and only 3ha had good conditions. Statistical analysis indicated a high variability in soil chemical properties. Properties such as pH and bulk density showed low variability.

Quantifying and Mapping of Major, Secondary and Micronutrient Status of Tomato Growing Soils in Kolar District, Karnataka Using GIS and GPS Approach

Assessment of land use-induced changes in soil properties is essential for addressing issues of spatial variability in soil fertility and sustainable land productivity. In view of this, a study was conducted to assess the impact of spatial variability on physicochemical properties, macro and micro nutrient status of 75 farmers fields of tomato growing areas of Kolar district, Karnataka. Arc Map with spatial analyst function of Arc GIS software was used to prepare soil fertility maps, which would act as an important tool for soil as well as nutrient management for sustainable crop production by using Global Positioning System coordinates. The results revealed that the soils under investigation were acidic to alkaline in reaction (pH 4.41 to 8.13), mostly non saline and low to high in organic carbon status (0.53 to 1.95%). Available nitrogen, phosphorus, potassium and sulphur content varied from 150.53-348.10, 6.4 - 111.44, 147.18 - 916.61 and 12.29 - 103.8 kg ha-1. Exchangeable Ca, Mg are in the range of 2.5 – 14.2 and 1.2 – 6.6 C mol (p+) kg. The GIS-aided thematic maps with respect to available zinc, 6.67 (low), 21.33 (medium) and 72 (high) per cent samples were categorized, based on the existing critical limits. All the studied samples i.e., 100 per cent were high in copper content, 65.33, 30.67 and 4 per cent samples of Mn, 25.33, 32 and 42.67 per cent samples of Fe, were categorized as low, medium and high in nutrient status respectively. With respect to available boron 16.67 per cent was under low, 38.09 per cent was under medium and 45.24 per cent was under high nutrient status. The nutrient index (NI) of tomato growing areas of Kolar district revealed that N (1.19), manganese (1.39) and iron (1.29) were categorized as low, available boron (2.17) as medium and P (2.65), K (2.71), S (2.65) zinc (2.75), copper (3.0) as high nutrient index category.

Spatial variability in soybean associated with soil fertility variations in a no-tillage system

Understanding the spatial variability of soil fertility is necessary for preventing nutrient losses or excessive agricultural inputs. The aim of this research was to evaluate the spatial variability of the chemical characteristics of a Typic Hapludox cultivated with soybean for 110 days at different depths and the impacts of these characteristics on crop yield. Soil samples were collected at a total of 80 points in an area of 5000 m2 . The contents of P, Ca, Mg, S, Cu, Fe, Zn and Mn were analyzed, in addition to pH and H+Al. The data were evaluated through descriptive statistics and geostatistical tools, and kriging maps were made based on semivariogram adjustments. Most of the soil fertility variables showed moderate or strong spatial dependence. The statistical moments obtained for the distributions showed that the symmetry of the distributions allowed the use of geostatistics techniques. In general, the greater the soil depth was, the lower the nutrient levels were. The soybean yield range (29.5 m) was similar to the P range in the 0.0-0.20 m soil layer (29.2 m). P influenced the soybean yield the most, as expressed by the similarity between the two kriging maps. In regions with more phosphorus at deeper soil layers, higher soybean yields were obtained.

Assessment of spatial variability of soil fertility status in KVK farm of Pahanda in Durg district, Chhattisgarh using Gis-Gps

The precise nutrient management is one of the most important and integral part of the modern-day agriculture. For this to happen we have to know the spatial variability of soil fertility. In this study, all soil physico-chemical properties (pH, EC, OC), Available macronutrients (N, P, K, S) and Available micronutrients (B, Fe, Mn, Cu, Zn) status with their spatial variability in KVK farm of Pahanda was quantified and based on their result soil fertility maps were prepared for respective nutrients using Arc-GIS software. Total 52 soil samples from soil surface (0-15 cm) collected from the study area and analyzed for these parameters. Soil reaction of the study area varied from neutral to saline range with EC below

Influence of Landform on the Spatial Variability of Soil Fertility in Central State Farm, Jetsar, Sri Ganganagar district of Rajasthan

Understanding the spatial variability in soil parameters is important for efficient management of soil resources. The present study evaluated the influence of landform on the spatial variability of chemical and physical attributes of soils of Central State Farm, Jetsar, Sri Ganganagar district, Rajasthan. The highest available macro and micronutrients content were observed in soils of aeo-fluvial plain followed by stabilized sand dune and active sand dune. The soils across the landforms were low in organic carbon content barring 3 per cent soil samples of aeo-fluvial plain. The mean available N was 153, 139 and 112 kg ha-1 in aeo-fluvial plain, stabilized sand dune and active sand dune, respectively. The available P in stabilized sand dune and aeo-fluvial plain was found high whereas it was medium in active sand dune. About 41, 22 and 17 per cent soil samples of aeo-fluvial plain, stabilized and active sand dunes were high in available K, respectively.

Spatial variability of soil fertility in a coconut based agro ecological unit in the sandy plains of Kerala, India

Spatial variability of soil fertility in a coconut based agro ecological unit in the sandy plains of Kerala, India

Assessment of spatial variability in fertility status and nutrient recommendation in alanatha Cluster Villages, Ramanagara District, Karnataka using GIS

Knowledge of spatial variability in soil fertility is important for site-specific nutrient management. In this study, spatial variability in properties that influence soil fertility such as soil organic carbon (OC), available nitrogen (N), available phosphorus (P), available potassium (K) and secondary and micronutrients in surface soils (0–30 cm depth) of 67 farmers’ fields of Alanatha Cluster Villages, Kanakapura Taluk, Ramanagara District, Karnataka was quantified and the respective thematic maps were prepared on the basis of ratings of nutrients. Arc map with spatial analyst function of Arc GIS software was used to prepare soil fertility maps. Soils were strongly acidic to slightly acidic in reaction with normal electrical conductivity (EC). Soil OC content was low to high, available N low to medium and available P and available K low to high. The soil of sampled area were found to be 100 per cent sufficient in exchangeable calcium, 97 per cent area was found to be sufficient in exchangeable magnesium. Available sulphur status was found to be low to high. The entire area was found to be sufficient in available manganese and copper, 86.5 per cent area was sufficient in zinc and 85.0 per cent area was sufficient in iron in surface soils. The observed spatial variability in soil properties that influence soil fertility was used for deciding nutrient application to finger millet and groundnut. Thus, providing balanced nutrients to crops based on soil of fertility status resulted in enhanced crop productivity and net returns in both groundnut and finger millet cropping systems.

Fuzzy logic and geostatistics in studying the fertility of soil cultivated with the rubber tree

Knowledge of the spatial variability of soil attributes is an aid to soil management. The aim of this study was to apply fuzzy logic and geostatistics in defining the spatial variability of soil fertility, in soil cultivated with the rubber tree. Soil samples from the 0-0.20 m and 0.20-0.40 m layers were collected in a stratified random sampling grid, with a shortest distance of 6 m, for a total of 60 points. The chemical attributes were P, K, Ca, Mg, BS, CEC and V. The spatial dependence of the attributes was determined, and the maps were constructed using ordinary kriging interpolation. The interpolated values were transformed into degrees of pertinence (FI), with the lower and upper limits previously defined, using an increasing model. The nutrient P displayed values in both layers below the recommended lower limit ( 50%. This methodology reduced the number of maps for interpreting soil fertility in the area, enabling visualisation of the spatial and gradual variability of the needs of the region.

Application of Geographical Information System to Understand Spatial Variability of Soil Available Nutrients in Northern Karnataka, India

It is very important to distinguish the spatial variability in soil fertility for site specific nutrient application. To know the status, 25 soil samples were collected from Vandurga Village, Yadgir District, Karnataka, India. Samples were analysed for electrical conductivity (EC), power of hydrogen (pH), organic carbon (OC), Nitrogen (N), Phosphorous (P2O5) and Potassium (K2O). Further, SPSS (ver. 19) was used to execute conventional statistical analysis and ArcGIS to get the information about distribution and spatial variability of soil available nutrients. The analysis results showed that the EC of soil varied from 0.13 to 0.25 dS/m with a mean of 0.18 dS/m. The PH ranged from 6.62 to 8.82 with an average of 7.89. Available OC ranged from 0.14 % to 1.90 % with mean of 0.78 %. Similarly mean values for N, P2O5 and K2O observed 215.3 kg/ha, 31.5 kg/ha, and 513.4 kg/ha, respectively. The SD and CV for EC was 0.031 and 16.69%, respectively, while for pH, OC, N, P2O5 and K2O it was found to be 0.56 and 7.04, 0.39 and 51.16, 100.9 and 46.86, 19.12 and 60.61, 160.88 and 31.33 respectively. Spatial variability maps for various nutrients prepared shows the huge variation in the soil nutrients availability. This variability appeared due to lack of balanced application of fertilizers. It was suggested that an appropriate applications of nutrients necessary for selected land based on soil nutrients.

Spatial variability of soil fertility and its relation with cocoa yield

ABSTRACT The knowledge on the spatial variability of soil properties and crops is important for decision-making on agricultural management. The objective of this study was to evaluate the spatial variability of soil fertility and its relation with cocoa yield. The study was conducted over 14 months in an area cultivated with cocoa. A sampling grid was created to study soil chemical properties and cocoa yield (stratified in season, off-season and annual). The data were analyzed using descriptive and exploratory statistics, and geostatistics. The chemical attributes were classified using fuzzy logic to generate a soil fertility map, which was correlated with maps of crop yield. The soil of the area, except for the western region, showed possibilities ranging from medium to high for cocoa cultivation. Soil fertility showed positive spatial correlation with cocoa yield, and its effect was predominant only for the off-season and annual cocoa.

Differential vineyard fertilizer management based on nutrient,s spatio-temporal variability

Conventionally, vineyard fertilizer management has been based on information from composite soil samples and no account has been taken of the existing spatial variability in soil fertility. This study presents a quantitative analysis of soil phosphorus (P2O5) and potassium (K2O) content as well as pH carried out in an 80 ha vineyard, during 2011 and 2013 in order to identify their spatial variability and temporal stability. Additionally a quantitative analysis of plant P2O5 and K2O content was carried out in 2013 with the objective of evaluating the spatial variability of plant nutrients.In 2013 a contact sensor was used to survey soil apparent electrical conductivity (ECa) and an active optical sensor was used to measure the plant Normalized Difference Vegetation Index (NDVI). The results showed a low potential for implementing site-specific management of phosphorus fertilizer but an interesting potential for implementing site-specific management of potassium fertilizer and pH correction. The concentration of P2O5 and K2O in the plant showed a CV<30%, with adequate values in almost the entire area of the field, in contrast to the concentration of these main macronutrients in the topsoil. These results show that for differential nutrient management of vineyards, plant nutrient concentration is a more stable tool than soil nutrients concentration. The ECa and the NDVI presented weak correlations with soil and plant concentration of, , respectively, P2O5 and K2O, which shows that further development of vegetation operational sensors is needed to support decision making in the vineyard fertilization management.

Spatial variability of soil fertility in an integrated crop livestock forest system

Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

Spatial variability of Soil fertility using soil chemical data, Atrush area, Duhok, Kurdistan region, Iraq

One of the basic factors in sustainable crop production is enhancing soil quality based on the soil fertility and returning absorbed nutrition by plants to the soil. In this spatial research variability of soil properties in Atrush area in Duhok province, Iraq was conducted based on the 15 soil samples with randomized method were taken from different locations. Statistical analysis by SPSS and spatial distribution and interpolation of soil properties by GS+software was done. Results showed all studied soil parameters such as pH, EC, %OM, percent of sand, silt and clay, %total N, available P and K hadanormal distribution. Strong spatial dependence related tothepercent of organic matter, sand and clay percent, percent of total nitrogen, available phosphorus and potassium while pH, EC, and percent of silt showed moderate spatial dependence. The variogram model were spherical for pH, EC, %OM, %clay and % total nitrogen while for sand percent and available P were linear and for available K and percent of silt were exponential. Spatial interpolation accuracy and precision by MAE, MBE and RMSE showed Kriging prediction maps for pH, EC, %OM, %N, and available K had most accurate predictions with the lowest error, therefore, it can provide useful information about urban planning and environmental management.

Spatial variability of soil nutrient in paddy plantation: Sites FELCRA Seberang Perak

The conventional methods currently used for rice cultivation in Malaysia are unable to give maximum yield although the yield production of paddy is increasing. This is due to the conversional method being unable to include soil properties as one of their parameters in agriculture management. Soil properties vary spatially in farm scale due to differences in topography, parent material, vegetation or land management and soil characteristics; also plantation productivity varies significantly over small spatial scales. Knowledge of spatial variability in soil fertility is important for site specific nutrient management. Analysis of spatial variability of soil nutrient of nitrogen (N), phosphorus (P) and potassium (K) were conducted in this study with the aid of GIS (i.e ArcGIS) and statistical softwares. In this study different temporal and depths of soil nutrient were extracted on the field and further analysis of N,P,K content were analysed in the chemical laboratory and using spatially technique in GIS sofware. The result indicated that for the Seberang Perak site of 58 hactares area, N and K are met minimum requirements nutrient content as outlines by the MARDI for paddy cultivation. However, P indicated poor condition in the study area; therefore the soil needs further attention and treatment.

Spatial variability and geostatistics application for mapping of soil properties and nutrients in intensively cultivated village of Veeranam Command Area, Tamil Nadu

Knowledge of spatial variability in soil fertility is important for site specific nutrient management. In the current study, the spatial variations of soil pH, organic carbon, N, P, K, Fe, Cu, Mn and Zn were evaluated in Orathur village (Typic Haplusterts) of Veeranam command area of Cuddalore district, Tamil Nadu. Global positioning system (GPS) guided surface soil sampling (0-15 cm) were done over an area of about 100 hectares in a 100 x 100 m grid. Wide ranges of variation were noted for different soil physico-chemical properties as well as nutrient status in the study area. The pH of soils ranged from 6.70 to 8.30, organic carbon ( %) varied from 0.49 and 0.88, available nitrogen (kg ha) from 173.60 to 235.20, available phosphorus (kg ha) content varied from 4.90 to 17.10, available potassium varied between 180.00 to 330.0 kg ha, while available S content varied from 3.10 to 21.30 ppm. The available Fe and Mn varied from 3.26 to 20.16 ppm and 0.94 to 8.26 ppm. The variation of soil nutrient contents across the study area was found to be related to the fertilizer use pattern among farmers. The evaluated physico-chemical and nutrient status data of the area were used to develop variability maps with the aim of generating site –specific nutrient management strategies.