Apple has prominent place in fruits crops globally and are grown on 4.72 million ha with production of 87.24 million tons
The overall soil physical, chemical and biological properties are imperative in maneuvering soil quality indicators that have close link with inconsistency in soil productivity
Soil nutrient status in different apple orchards was evaluated by group of researchers
The application of fertilizer by traditional methods in places with varying nutrient status is now considered unsuitable and inefficient for the reason that from place to place soil fertility varies expressively. Unwise fertilizer application i.e. overapplication can of course be a wastage of fertilizer with hazardous environmental consequences. Geostatistical tools have been proved most effective in showing the distribution and pattern of spatial variation of soil nutrients and chemical properties
District Ziarat is comprised of 1487 km2 area and one of the smallest districts of the province located at 30°22′51 North and 67°43′37 East at an altitude of 2453 meters (8050 feet). The world’s largest Juniper Forest makes Ziarat famous for the second largest Juniper forest in the world. Administratively the district has two tehsils with seven union councils and situated in east of Quetta at a distance of 70 km (GoB, 2012). It is mountain locked district distributed into several valleys with altitude of 1,800 to 3,488 meters. The prominent vallys are Kach, Kawas, Ziarat, Zandra, Mangi, Mana and Gogi Ahmadoon. The land area of district can be categorized as flood land, plain land and stony land. The flood water coming from high mountains areas along with mixed material deposited at the lower end forming flood lands. The natural terraces on the mountainous area later developed into plain lands. The stony lands have not been used for cultivation. Parent material of the area is shallow containing lime, along with coal, marble (dimensional stone), calcite and laterite (titanium). Laterite deposits contain only one to two percent titanium oxide along with high iron and aluminum contents which stretch over an area of 65 kilometer from Ziarat towards Sanjavi. Major area of the district land has been under apple orchard cultivation along with other fruits such as cherries, plums, apricots, almonds, grapes, etc.
Soil samples were collected with help of soil auger from 50 apple orchards of two tehsils (Ziarat and Sanjavi) and seven union councils (Kach, Kawas, Ziarat Sanjavi, Mana, Warchm and Poi) of district Ziarat. Under Ziarat tehsil, 5 orchards were selected each from Ziarat and Zandra,10 from Kawas, 9 from Mana and 5 from Warchum. Under Sanjavi tehsil, 13 orchards were collected from Sanjavi, and 3 from Poi union councils. A total of 1600 soil samples (0-30 cm) were collected from 50 apple growing orchards of district Ziarat in the month of August. GPS coordinates of each location in the apple orchards were recorded. To fulfill the criteria of representative sampling, 8 trees were randomly selected in each apple orchard. Four soil cores were dug below each of the 8 apple tree canopy and composited into one sample. Transparent polyethylene bags were used to preserve the soil samples. The soils samples were unpacked, air-dried and ground using wooden pestel mortar. Finally, the samples were sieved through 2 mm sieve. The sieved samples were transferred into air-tight plastic jars.
Soil samples were analysed for texture, organic matter, pH, EC, macro and micro nutrients (N, P, K, Cu, Fe, Mn and Zn) concentration. Soil texture of soil samples was carried out by Bouyoucos hydrometer method
Descriptive statistics including minimum, maximum, mean, mode, standard deviation (SD), and coefficient of variation of chemical properties was carried out using Microsoft Excel.
The special variation of Kjeldhal’s N, and AB-DTPA extractable nutrients i.e. P, K, Cu, Fe, Mn and Zn in the apple orchard soils was assessed by the kriging interpolation method. The Ordinary Kriging Model
Here, γ(h) described the magnitude of the lag distance between the two sample locations. While N(h) described the number of observation pairs, separated by h (the distance), Z (the random variable) and x is the random variable at specific location.
Three parameters nugget variance (Co), sill (Co + C) and range (a) were used to illustrate the structure of model in semivariogram analysis. Among different functions i.e. stable, circular, spherical, exponential and Gaussian, the semivariogram uses the suitable model. After applying the function, best fitted mathematical models of semivariogram were selected for assessing the integrity of the model to the data. The prediction errors such as mean, root mean standard square error (RMSSE), average standard error (ASE) and root mean square error (RMSE) were calculated and analyzed by using the Geo-Statistical software (GS+). Predictive maps of soil nutrients (i.e. N, P, K, Cu, Fe, Mn and Zn) were generated using a semivariogram model through ordinary kriging, which was used further to transform the sampling location data into incessant fields. This was carried out by ArcGIS 8.1.
Soil properties are influenced by a variety of factors including soil parent material, topography, climatic conditions, soil biota, and other soil physical and chemical processing factors. The soil of apple orchards across district Ziarat was studied for soil fertility indicators included soil texture, organic matter, pH, EC, Total N, AB-DTPA extractable macro (N, P and K) and micro (Cu, Fe, Mn and Zn) nutrients. The spatial variability and spatial dependence of these variables were investigated using geostatistical analysis except soil texture and organic matter.
The proportion of soil fractions into sand, silt and clay (
Soil parameters |
Minimum |
Maximum |
Mean |
Mode |
SD |
CV% |
Sand (%) |
8.30 |
73.70 |
27.97 |
18.70 |
18.70 |
66.86 |
Silt (%) |
9.20 |
79.60 |
57.66 |
72.10 |
17.58 |
30.49 |
Clay (%) |
7.10 |
26.70 |
14.39 |
14.60 |
4.65 |
32.29 |
Organic Matter (%) |
0.31 |
2.91 |
1.39 |
- |
0.16 |
11.81 |
Electrical conductivity (dS m-1) |
0.23 |
0.65 |
0.40 |
0.36 |
0.23 |
58.40 |
pH |
7.71 |
8.45 |
8.05 |
7.93 |
0.16 |
2.04 |
Kjeldahl’s N (%) |
0.02 |
0.15 |
0.07 |
- |
0.03 |
41.54 |
Phosphorus (mg kg-1) |
0.26 |
9.41 |
3.10 |
2.02 |
2.07 |
66.54 |
Potassium (mg kg-1) |
21.0 |
203.6 |
67.0 |
57.75 |
31.69 |
47.27 |
Copper (mg kg-1) |
0.21 |
1.35 |
0.68 |
0.53 |
0.27 |
39.61 |
Iron (mg kg-1) |
0.25 |
6.29 |
1.54 |
- |
1.24 |
80.10 |
Manganese (mg kg-1) |
0.41 |
65.97 |
8.86 |
- |
11.80 |
133.12 |
Zinc (mg kg-1) |
1.80 |
27.01 |
5.11 |
4.83 |
3.71 |
72.63 |
Total organic matter content was typical of our soils and was as low as 0.31% and as high as 2.91% with an average value of 1.39%. Mode values showed that majority of the values were close to average values (
The data (
Soil nutrients |
Critical limits |
Percent sample |
||||
Low |
Medium |
Adequate |
Low |
Medium |
Adequate |
|
Organic matter (%) |
<0.86 |
0.86-1.29 |
>1.29 |
16 |
30 |
54 |
Kjeldahl’s N (%) |
<0.05 |
0.05-1.0 |
>1.0 |
38 |
46 |
16 |
Phosphorus (mg kg-1) |
<4.0 |
4.0-7.0 |
>7.0 |
72 |
24 |
4 |
Potassium (mg kg-1) |
<60 |
60-120 |
>120 |
50 |
44 |
6 |
Copper (mg kg-1) |
<0.2 |
0.2-0.5 |
>0.5 |
- |
26 |
74 |
Iron (mg kg-1) |
<2.0 |
2.1-4.0 |
>4.0 |
74 |
20 |
6 |
Manganese (mg kg-1) |
<1.8 |
- |
>1.8 |
10 |
- |
90 |
Zinc (mg kg-1) |
<1.0 |
1.0-1.5 |
>1.5 |
- |
- |
100 |
Soil pH is the main driving force in biogeochemical processes that occurs in soil system and is considered as master variable in soil fertility indicators responsible for affecting the soil properties, nutrient availability and crop productivity. In this study, it was observed that the pH values varied within a narrow range of 7.71 and 8.45 with an average value of 8.05. The mode value of 7.93 depicted that majority of the values were close to average values and this was very clear from the coefficient of variability (
For better plant growth and soil health, the existence and availability of plant essential nutrients is prerequisite
In case of micronutrients, all the orchards' soils showed copper, manganese and Zin in adequacy ranges except iron which was low in 74% orchards (
Geostatistical analysis is one of the important statistical tools which is principally used for assessment and mapping of soil characteristics at location of unsampled area. Spatial variability and dependence of soil parameters were determined by semivariogram and ordinary kriging. Variation caused by experimental error or smaller sampling scale is expressed by nugget. When the value of nugget is bigger than the minor measure process cannot be overlooked. The ratio of nugget and sill reveal variation in percentage instigated by stochastic factors to the total variation of the system. Actually, the ratio of nugget and sill shows autocorrelation among various parameters. Strong spatial autocorrelation of a variable is characterized by lower nugget and sill ratio (<25%), moderate spatial autocorrelation is exposed by medium nugget and sill ratio (25-75%) and weak spatial autocorrelation is signified by higher nugget-sill ratio (>75%) respectively
Characteristics |
Applied Model |
Range A0 |
Nugget:Sill |
Root Mean Square Standardized Error |
Average Standardized Error |
Root Mean Square Error |
EC |
Stable |
0.11 |
50.00 |
0.88 |
0.28 |
0.25 |
pH |
Stable |
0.00 |
85.71 |
1.01 |
0.17 |
0.18 |
Nitrogen |
Stable |
0.61 |
75.0 |
1.04 |
0.02 |
0.02 |
Phosphorus |
Stable |
0.01 |
10.64 |
0.99 |
1.95 |
1.90 |
Potassium |
Stable |
0.00 |
3.76 |
1.01 |
35.05 |
34.74 |
Copper |
Stable |
0.08 |
85.36 |
0.95 |
0.22 |
0.20 |
Iron |
Stable |
0.09 |
8.84 |
0.85 |
1.26 |
1.05 |
Manganese |
Stable |
0.16 |
61.17 |
1.05 |
13.25 |
13.27 |
Zinc |
Stable |
0.12 |
42.41 |
1.24 |
3.54 |
4.31 |
The semivariogram of soil macronutrients (N, P and K) using stable model expressed distance of spatial dependence of 0.61, 0.01 and 0.00 km for N, P and K (
The Variogram analysis showed that AB-DTPA extractable soil micronutrients included Cu, Fe, Mn and Zn were better characterized by semivariogram with stable model that expressed distance of spatial dependence of 0.08, 0.09, 0.16 and 0.12 km for Cu, Fe, Mn and Zn (
The statistical analysis revealed that soils of apple orchards were mostly silty loam, non-saline and alkaline. Soil organic matter content was in medium to high in majority of the orchards along with adequate level of TN, Cu, Mn and Zn but P, K and Fe were in low ranges because the growers who apply manures don't use chemical fertilizer except nitrogen fertilizer indicating imbalance fertilization. The spatial structure of all physico-chemical properties, plant available macro and micronutrient was explained by a stable model. Soil pH, Cu and Mn had a weak spatial dependence while, soil OM, TN, Mn and Zn had a medium spatial dependence. But soil P, K and Fe had a higher spatial dependence. This study demonstrated that the variability of soil chemical properties was associated to the management practices (fertilizer, irrigation, and other tillage practices) and local conditions (topography, climate etc.). The main limitation observed in this study was insufficient sampling from a single tree and a greater number of trees in each orchard need to be included in sampling for making representative composite sample. The data regarding use of fertilizers and composts in the apple orchards are required to confirm the actual status of nutrients during spatial variability study. Furthermore, the replenishment of nutrients particularly P, K and Fe is necessary in the study area. The findings of this study can help in better understanding of soil properties of apple orchards in district Ziarat and site-specific fertilizer recommendation can be made for sustainable apple production with minimum losses and greater output.