Ninety percent of the plant dry matter is composed of carbon (C), hydrogen (H) and oxygen (O), the remaining include the essential nutrients i.e. nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulphur (S), boron (B), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn) (Epstein and Bloom, 2006) which are applied in the form of mineral fertilizers except in case of Ca, Mg and S, as Pakistani soils are already rich in these nutrients. Sugarcane (Saccharum officinarum L.) being a long duration and exhaustive crop, removes 122 kg N and 142 kg P from soil with a cane yield of 85 t ha-1 (Bokhtiar et al., 2001) and obviously requires greater quantities of essential nutrients (Rice et al., 2008). Formulation of sound fertilizer program forms the basis for achieving yield targets.
The average cane yield of 57.55 t ha-1 as given by Pakistan Sugar Mills Association (PSMA, 2014) is much lower than the potential yield of 150-200 t ha-1 obtained at research stations and progressive growers’ farms. The highly calcareous nature of soils, elevated levels of soluble salts, low organic matter content and macro and micro nutrient content in soil (Rashid and Ahmad, 1994; Izhar-ul-Haq et al., 2007) are some of the factors responsible for obtaining low yields. The supply and availability of nutrients adversely influence the yield of field crops widening the gap between actual and potential yields (Meynard, 1984). The management factors including low and imbalanced nutrient use efficiency with intensive crop cultivation is further mining the nutrients, and therefore aggravating nutrient deficiencies which ultimately result in poor cane yield (Akhtar et al., 2003; Abbas et al., 2011).
Addition of nutrients in the field is achieved through application of mineral fertilizers, organic residues, green manures, bio-fertilizers and irrigation water. Nutrient recycling through crop residues may be important pathway, the mineral fertilizer as nutrient source remains main stake for most food production systems. Potential nutrient supply from soil is assessed through soil and plant analysis (McCray et al., 2013). The fertilizer recommendations normally formulated are based on the soil analysis compared to established critical limits of bio-available nutrients indicated, which, however, may not relate to the plant tissue nutrient concentration due to several biotic and abiotic factors (Rice et al., 2008). Assessment of nutrient levels in plant becomes a pre-requisite to determine whether nutrient demand for optimum crop production is being met or additional nutrients are required (Haase and Rose, 1995). The nutritional requirement of crops and nutrient deficiency diagnosis is achieved through plant analysis. Plant nutrient analysis requires a careful interpretation using the critical level approach (McCray et al., 2013). The interpretation, also referred as evaluation of the analytical results, is of decisive importance. There are still difficulties in the interpretation of plant analysis, despite the fact that relationships have been established between absorbed nutrients and growth parameters. The relationships between concentrations of essential elements in the plant tissue and maximum yield has since long been the subject of research (Liebig, 1840; Lofton et al., 2012).
Different methods for the interpretation of chemical plant analyses are proposed (Steenbjerg, 1951; McCray et al., 2013) and the relationship between the increase in yield after application of nutrients and the nutrient concentrations in the plant has been illustrated (Fageria and Baligar, 2005a, Fageria et al., 2009). Plant analysis interpretations are based on “critical” or “standard values”. The critical levels vary in plants and nutrients with age but occur between nutrient deficiency and sufficiency range (Fageria et al., 2009). Critical values of several plants have been widely published despite the fact that critical level may not be at all growth stages (Rosell et al., 1992; Fageria et al., 2009). The critical level approach was one of the first methods proposed by Ulrich and Hills (1967) for assessing the nutrient status by plant analysis. Single concentration values or critical or standard concentrations were used (Smith, 1962). Critical nutrient concentration is the level of a nutrient below which crop yield, quality and or performance is unsatisfactory (Benton, 1993). Use of a critical nutrient range defined as range of nutrient concentration at a specified growth stage above which the crop is amply supplied with the nutrients and below which the crop is deficient in specific nutrients (Kelling et al., 2000; Brady and Weil, 2002; Tisdale et al., 2002; Havlin et al., 2004).
In an interpretation concept developed by Okhi (1987), the critical nutrient level is that nutrient concentration level at which a 10% reduction in yield occurs; this level is also defined as the critical deficiency level (CDL). The adequate nutrient concentration has been defined in several ways. Fageria and Baligar (2005b) defined it as, i) the concentration that is just deficient for maximum growth, ii) the point where the growth is 10% less than the maximum, iii) the concentration where plant growth begins to decrease, and iv) the lowest amount of the element in the plant accompanying the highest yield.
The problem with critical level approach is that nutrients concentration in plant changes with plant age, for example N moves from older leaves to younger leaves. The ratios of nutrient however, may change less over development than the absolute concentrations. The approach of nutrient balance instead of concentrations formed the bases for Diagnosis and Recommendation Integrated System (DRIS). This approach first developed by Beaufils (1971) was applied to rubber trees in Vietnam. Later, DRIS went through several phases of improvement (Jones and Bowen, 1981; Walworth and Sumner, 1987).
DRIS compares the indices elemental ratio with the established norms from an optimum high-yielding population. Walworth (1986) based the DRIS norms on several thousand entries of plant nutrient analysis and yield, which were randomly selected. Benton (1993) had top 10% of the total entries selected as the high yielding group, and the nutrients were ranked according to the degree of limitation (or excess). International norms (nutrient ratios for the high yielding population) have been reported (Sumner 1981, Beverly, 1993). Some researchers suggested that norms calculated from a local data base may improve diagnosis through DRIS (Dara et al., 1992).
The DRIS approach has been applied to sugarcane for diagnosis of the N, P and K requirements (Beaufils and Sumner, 1977). They used a field experiment data on NPK + lime for testing DRIS for sugarcane and reported a valid diagnosis of N, P and K requirements of sugarcane. Gregoire and Fisher (2004) investigated the relative benefits of different approaches for the diagnosis of nutrients and compared the vector analysis, DRIS and critical level approaches for identifying deficiencies of N and P by means of foliar analysis in 60 managed stands in southeast Texas. The diagnostic efficacies differed in prediction of the response to fertilization and no method alone was accurate enough for precisely predicting the response across soil groups. Further, it was concluded that all the three methods of interpretation had their advantages.
National fertilizer development center reported that N application is the most prevalent practice. Farmyard manures and crop residue incorporation declined with time (NFDC, 2003). Fertilizer cost, its availability and the expected value of the produce influence farmers’ decisions regarding the fertilizer and the quantity to be applied. Increase in the prices of P fertilizers resulted in its reduced use. Such shifts in fertilizer use adversely affect plant nutrition and the crop yields.
Plant nutrition surveys have established N, P, K, Zn, B, Fe deficiencies in a number of field crops (Rashid and Ahmad, 1994). Phosphorus is deficient in 80-90 % of the soils despite the use of P fertilizers in the last four decades. Potassium levels are generally adequate in majority (60%) of soils and some 40% soils are marginal to deficient. Organic matter level is very low (<1.0%), and in 75-80 % of the soils even below 0.5%. Analysis of soil samples indicated that Cu, Fe and Mn levels were generally adequate for crop nutrition, however, Zn was the most deficient micronutrient in Sindh soils, and on average more than 50% of the soils were deficient in Zn (Memon, 1986).
Importance of plant index tissue analysis at grand growth stage has been highlighted as a more reliable means of estimating nutrient deficiencies in sugarcane. The CNL refers to the concentration of a nutrient in the particular part of the plant at a specific stage when production losses reach 5 to 10 %. The CNL approach also include nutrient optimum range, defined as the range of concentration optimum for production. Within this range, there should be no deficiency or excess of a given nutrient (Mabry et al., 2006). Consequently, diagnosis based on foliar analysis, that a particular nutrient is limiting in the plant does not necessarily mean that the nutrient is also limiting in the soil. Drought, water logging, low temperatures and pests attack can alter leaf composition without any change in the nutrient levels in the soil.
Secondly, the DRIS approach has particular benefits of taking nutrient balance into consideration in making a diagnosis and can also be applied without modification over a wider range of plant age than the CNL approach. Plant nutritional status is measured by comparing the leaf tissue nutrient concentration ratios of nutrient pairs with norms from a high yielding group. DRIS requires a large number of observations of plant tissue nutrient concentrations and associated crop yields; and are used to determine nutrient ratio means to separate low and high yielding populations. It uses the nutritional balancing concept (relationship among nutrients) and is considered more precise than the other methods detecting nutritional deficiencies or imbalances.
DRIS norms have been used in other countries for nutrient deficiency diagnosis and plant nutrition requirements in a number of cereal (wheat, maize), fruit (pineapple, tomato), vegetable (lettuce) and even ornamental crops (Creste et al., 2001; Hartz and Johnstone, 2007; Agbangba et al., 2011; Akhtar, 2012; Rheem Abd El et al.,2015) including sugarcane (Beaufils and Sumner, 1976; Morris et al., 2005; Galindeze et al., 2009; McCray et al., 2010). However, there is no local information available regarding DRIS for sugarcane in Pakistan.
Sugarcane is the largest cash crop grown in more than 90 countries of the world producing 1.7 billion tons from 24.3 m ha (FAO, 2013). It plays a vital role in the economy, provides a major share in farmers’ earnings, and contributes in socio-economic development of Pakistan. The country ranks fifth in sugarcane producing countries (Brazil, India, China, Thailand, Pakistan and Mexico). In Pakistan, sugarcane is grown on 1.17 m ha with an average yield of 57.55 t ha-1, out of which 0.297 m ha are grown in Sindh province constituting 25.38% of the total sugarcane area (PSMA, 2014). The potential cane yields are between 150-200 t ha-1 for Sindh, 100-150 t ha-1 for Punjab and 75-100 t ha-1 for NWFP (Malik, 1990; 2010). It clearly shows a huge yield gap between national average sugarcane yield and that obtained at research stations and from progressive farms.
Sugarcane is a long duration and exhaustive crop, relatively requiring large quantities of essential nutrients with balanced supply of N, P, K, B, Cu, Fe, Mn and Zn (Bajwa, 1990; Paul et al., 2005; Rice et al., 2008). It removes 122 kg N and 142 kg P from soil with a cane yield of 85 t ha-1 (Bokhtiar et al., 2001; Sarwar et al., 2010).
Furthermore, the soils of Pakistan are deficient in major nutrients with 100% in N, 90% in P and 50% in K, in addition to widespread micronutrient deficiencies (Bajwa, 1990; Khalid et al., 2012; Memon, 2012). Most of the soils in Pakistan have meager status of available plant nutrients and cannot support optimum levels of crop productivity (Rafiq, 1996; Ahmed and Rashid, 2003; Khalid et al., 2012). Other than this, as a custom, only few growers of Pakistan are inclined to include K in their fertilizer program. The fertilizer offtake for crop production is N 79, P 19 and K 0.8% (NFDC, 2013).
Productivity is declining world over due to continuous nutrient mining through intensive agriculture with high yielding crops (Rafique et al., 2006), imbalanced nutrient use with intensive crop cultivation has led to nutrient deficiencies in crops resulting in poor yield (Reynolds et al., 1987; Akhtar et al., 2003; Abbas et al., 2011). Memon et al. (2012) reported that soils of Badin, Sindh under tomato were low in Zn (1.5 mg kg-1) and B (1.0 mg kg-1). A declining trend in sugarcane yields have been reported in Nawabshah, Patidan and Sakrand areas of Sindh. These fields were under sever deficiency of macro and micro nutrients (Arain et al., 2000).
Fertilizers play an important role in improving cane and sugar yields (Korndorfer, 1990; Elamin, 2007). Proper fertilizer management is the key factor in sugarcane production (Khan et al.,2005). Higher growth rate of sugarcane relates to the enhanced uptake of N, P and K (Nasir et al., 2000). Therefore, the supply and availability of required fertilizers at right time directly influence the yield potential and reduction in soil productivity which ultimately has threatened the food security in many parts of the World (Meynard, 1984; Karstens et al., 1992; Li et al., 2007; Ibrahim et al., 2008; Sarwar et al., 2008). Excess P application inhibits the transfer of soil Zn and results its deficiency due to possible precipitation of Zn3(PO4)2 (Agbenin, 1998; Alloway, 2008). Optimum P application significantly reduces the carbonate, organic and Fe oxide bound soil Zn, and increases the exchangeable and amorphous Fe-oxide-bound soil Zn. Similar antagonistic interactions among other nutrients have also been reported (Bierman and Rosen, 1994; Alloway, 2008). Proper N, P and K application can increase soil Cu, Mn and Zn availability and the concentrations in plant. Nitrogen strongly provokes growth, increases the canopy and intercept solar radiation (Milford et al.,2000; Semma et al., 2014). Relatively large amount of K is required to maintain the necessary cell turgor for N stimulated growth (Wood, 1990; Gaffar et al., 2010; Semma et al., 2014). Very common world around NPK fertilizer rations in sugarcane production as reported by Wood (1990; Semma et al., 2014) are 2:1:3, 2:1:2 and 3:1:5. According to Larson (1964), N and P application increases the mutual effectiveness of both nutrients if applied in 2:1 or 3:1 ratio. Hagras (1987) and Gaffar et al., (2010) also reported such results related higher yields when applying similar fertilizer ratios.
The K and P fertilizers positively affect the quality of sugarcane (Elamin et al., 2007). The balanced use of N, P and K fertilizers increased plant height, cane thickness and number of tillers in sugarcane (Mahar et al.,2008). Balance NPK fertilization yielded as high as 165.2 t ha-1 (Sharif and Chaudhry, 1988; Malik, 1990; Khan et al., 2002). Therefore, it is concluded that imbalance use of fertilizers may not be meeting the nutritional requirement of the crop.
2.1 Spatial variability in soil
The variable application rate of inputs is important aspect of best soil management practices which makes use of spatial variability in soil properties and nutrients. The developments in geostatistics increased the ability to summarize and interpret soil data (Yost et al., 1982b; Bond-Lamberty et al., 2006; Loescher et al., 2014). Soil properties are determined on a grid with the assumption that properties measured at a point also give information for the unsampled neighboring area. The extent to which this assumption is true depends on the degree of existing spatial variability among samples.
The macro and micro nutrients vary spatially in soils related to the nature of the soil parent material and the soil’s position in the landscape. Berndtsson et al. (1993) investigated geostatistical properties of 20 major nutrients and trace elements in northern Tunisia. All elements had a clear spatial structure. Several elements displayed a significant trend that was removed by fitting the raw data to a second-order polynomial. Variograms for residuals showed that most elements reached sills at ranges of ~10 to 20 m.
Regional variation of selected topsoil properties in Sitiung, Indonesia were studied (Trangmar et al.,1984). Geostatistical analysis of spatial variation identified the presence of distinct directional patterns in sand, silt and clay in relation to depositional patterns of soil parent materials. Similar, but weaker, spatial patterns were also apparent in semi-variograms for soil pH, exchangeable Ca, Mg, Al saturation, and total P. Sand content of topsoil contained a distinct geological variation component with a range of spatial dependence of about 16 km. Ranges of spatial dependence were shorter (< 0.4 km or 3–5 km) for most chemical properties due to their sensitivity to shorter-range features, including leaching, erosion and soil management. Another study was related to fixed distance intervals of 500 m, and the maximum distance was set to 4700 m (Zhang et al., 2014).
Thus, geostatistical methods have been applied to soil studies over distances ranging from a few meters (Trangmar et al., 1987) to several (Trangmar et al., 1986) or many kilometers (Yost et al., 1982; Ovalles and Collins, 1988; Wei et al., 2006; Marcetti et al., 2012). For example, Yost et al. (1982a) concluded that soil chemical properties commonly have spatial dependence and understanding such structure may provide new insights into soil behavior over the landscape.
Semi-variogram analysis or variography, is based on the theory of regionalized variables. The importance of structural analysis using semi-variograms lies in its definition of parameters to be kriged (estimated), such as the degree of continuity and isotropy of the regionalized variable, the presence of trends (or drift), and range of spatial dependence. The application of regionalized variable theory assumes that semi-variance depends only on the direction and distance of separation between two sample sites and not on the actual locations of the sample sites. If this assumption is valid, then the semi-variogram for a region can be estimated from a single set of data (McBratney et al., 1982).
The semi-variogram illustrates the relationship between the semi-variance of samples and the distance or lag separating them. The semi-variance (h) is defined as:
(h) = (1/2) E[Z (xi) – Z(xi + h)]2 
where h is the lag distance separating pairs of points, E is the variance of the arguments, Z (xi) is the value of the regionalized variable (soil or crop property) at field location x, and Z (xi + h) is the value at the location xi + h. An estimate of (h) is given by:
(h) = [1/2n (h)] [Z (xi) –Z(xi+h)]2 
Where n(h) is the number of pairs separated by the lag distance h. A graph of as a function of various h constitutes the semi-variogram. An idealized semi-variogram is shown in Fig. 1. Theoretically, the semi-variance at a lag distance of zero equals zero. In a reality, as the lag distance approaches zero, the semi-variance usually approaches a finite positive value, the nugget variance. A non-zero nugget variance indicates sampling and analytical error and/or spatial variation at a resolution finer than the lag interval. Typically, the semi-variance increases with the increase in lag distance to approach or attain a maximum value or sill equivalent to the population variance. The maximum lag distance across which the data exhibit spatial correlation is the range. If spatial correlation depends only on distance and is independent of direction, it is isotropic. If spatial correlation varies with direction and distance, it is anisotropic (Trangmar et al., 1985; White et al., 1997; Bhatti, 2005; Wei et al., 2006; Marchetti et al., 2012; Zhang et al., 2014).
Burgess and Webster(1980) suggested that range in soil surveys will usually be a few hundred meters, and, exceptionally, two or three kilometers. However they pointed out that the range depends on the size of the sampled area. For example, over a large landscape on the island of Hawaii, Yost et al. (1982) observed that the range of pH was 14 to 32 km. Interpolation should preferably use points closer to the range.
Semi-variograms have been used to characterize spatial dependence of soil properties over many different scales of sampling. In a study in which P uptake by sorghum crop was measured on a 1.5 m grid, the range of modified Truog P was 5.6 m and that of leaf P was 6.1 m (Trangmar, 1982). On plots to which 45 kg P ha-1 was applied, spatial dependence of soil P decreased to 5 m and variance of leaf P became nonstationary as a result of trends in P uptake across the plots. The results suggest that soil management affects micro-variation of soil properties which, in turn, affects nutrient uptake variation in crop yield.
McBratney and Webster (1981) sampled soils at 20 m intervals along a transect in N.E. Scotland and identified dependence up to 360 m for soil color, pH, and little or
none for particle size fractions and organic matter content. Nested semi-variograms for some properties indicated soil variation at two different scales.
Trangmar et al. (1982) reported spatial dependence of about 4.0 km for exchangeable Na percentage in an area of Vertisols in Sudan. Trangmar et al. (1984) studied regional variation of selected topsoil properties in Sitiung, Indonesia. Geostatistical analysis of spatial variation identified the presence of distinct directional patterns in sand, silt, and clay in relation to depositional patterns of soil parent materials. Similar, but weaker patterns were also apparent in semi-variograms for pH, exchangeable Ca, Mg, Al saturation, and total P. Sand content of top soils contained a distinct geological variation component with a range of spatial dependence of about 16 km. Range of spatial dependence were shorter (<0.4 km or 3−5 km) for most chemical properties due to their sensitivity to short-range features, including leaching, erosion, and soil management.
Nayak et al. (2002) made quantitative estimates on the degree of spatial variation of surface and sub-surface soil salinity. They collected randomly 335 samples at random intersection of 1.75 km x 1.75 km of a square grid covering 25800 ha area of an irrigation block of Sardar Sarovar Canal Command in India. There was no effect of direction on surface and sub-surface salinity as indicated by the directional semi-variogram study. The isotropic semi-variograms of surface and sub-surface salinity best fit to spherical model structure. The range of spatial dependence of soil salinity is 6.64 km for surface, and decreases with depth.
The spatial variability of soil properties that affect the soil N budget and corn grain yield in south-central Texas helped to assess the potential for variable-rate N fertilization (Shahandeh et al., 2005). The residual soil NO3-N with depth and soil N mineralization potential were characterized, and their relationships with soil total N, soil organic C, and clay content were developed. Semi-variograms showed strong spatial dependence for soil NO3–N with depth and clay content in 2002, mineralized N in 2003, and total N in both years. Variograms with spherical and exponential models reached upper bounds, i.e. sills, suggesting that the properties varied in a “patchy” way, resulting in some areas with small values and others with large ones. The range of spatial correlation for each variogram provides an average extent of these patches. The range of spatial dependence for NO3–N with depth was 31−120 m.
The extent of temporal and spatial variability has been determined for sugarcane (Johnson and Richard Jr., 2005). Majority of soil properties in sugarcane fields in South Louisiana have non-normal distributions with coefficient of variation ranging from 1 to 56 % overall years and locations, and all soil properties were spatially correlated with the range varying from 26 to 241 m. Cane and sugar yields and quality parameters were spatially correlated with a range varying from 26 to 187 m, with the exception of theoretically recoverable sugar and fiber.
Lauzon et al. (2005) evaluated the scale of variability of soil test P, K, and pH for Ontario soils using autocorrelation analysis of 23 farm fields, which were grid-sampled using 30-m spacing. The results of the autocorrelation analysis indicated that 13 of the 23 farm fields would require a grid spacing of less than 30-m to adequate assess their spatial variability. For only one site, the commonly used 100-m grid spacing was adequate for the assessment of the spatial patterns of P and K. Further analysis using F tests compared the residuals from three gridding procedures (kriging, inverse distance and nearest neighbor) using 60 and 90 m grid data to that of the residuals using the field mean soil test value. In most cases, soil test variation maps based on 60 or 90 m grid soil samples did not result in an increased ability to predict the soil test level at a given location in the fields. They concluded that a grid spacing of 30 m or less would be required to adequately assess the spatial variation of soil test P, K and pH.
Thus, clearly the range of spatial dependence, the size of the nugget variance and isotropy characteristics are functions of both soil properties and scale of sampling. Differences in these parameters as a function of sampling scale indicate the “nested” nature of soil variability caused by interactions of soil forming factors and to a lesser extent, by differences in soil management over space.
2.1.2 Interpolation by Kriging
Kriging is a technique of optimal, linear, unbiased minimal variance estimation of regionalized variables at unsampled locations using the structural properties of the semi-variogram and the initial set of data values (Yost et al., 1982; Bond-Lamberty et al., 2006; Loescher et al., 2014; Zhang et al., 2014). The simplest forms of kriging involve estimation of point values (punctual kriging) or areas (block kriging). Variances are estimated using the modeled semi-variogram and the distance between the un-sampled and sampled points and interpolation weights for each contributing sampled point are determined so as to minimize the variance of the estimate. An estimation variance is provided for each estimated point which gives an indication of the reliability of the kriged value. When points have been estimated, they can be plotted on maps and joined by isarithm thus creating a map.
Punctual kriging (simple point estimation) is probably the most common kriging procedure used in soil science and the main use of punctual kriging in soil studies has been to produce iso-property maps and to optimally allocate additional sample sites to improve reliability of mapping (Trangmar, 1982; Trangmar et al., 1985; Bond-Lamberty et al., 2006; Loescher et al., 2014). Burgess and Webster (1980) mapped Na content by punctually kriging a grid of values at 7.6 m intervals using the nearest 16 data points for each interpolation. In the same study, they kriged cover loam at 6.7 m intervals to give a fine grid with nine times as many points as the original observation grid.
White et al. (1997) used punctual kriging to estimate and map total soil Zn throughout the conterminous USA. They presented only results obtained with a representative lag interval of 30 km because kriging from several of the semi-variogram models produced only a miner difference in the results.
Sampling is intended to represent the surrounding location, and wishes to interpolate an average value for an area or block larger than the cross-sectional area of the soil volume sampled. Local discontinuities can obscure longer range trends when point estimates are used for sampling. Detection of such discontinuities also depends on the locations of the sampling points and different maps may result from punctual kriging if different sampling schemes are used over the same area (Burgess and Webster, 1980). The shortcoming of punctual kriging is avoided by estimating average values over areas using block kriging, which results in smaller estimation variances and smoother maps (Trangmar, 1984).
Nayak et al. (2002) computed the kriged estimates by ordinary kriging using the original data and spherical semi-variogram model of surface and subsurface soil salinity of an irrigation block. They used estimated and observed values for preparing the contour maps. The average absolute difference between kriged estimates and that of the measured value was 0.020 in surface and 0.047 in the sub-surface soils. They used standard deviations of observed and kriged estimates for calculating the number of samples required to improve precision within + 10% of the true mean at 80, 90, 95 and 99 % confidence level.
The ordinary kriging considerably reduces sampling compared to classical technique. Grewal et al. (2001) reported that available P content of soils of Haryana, India was correlated over space for a separating distance of 61.5 m between two observations. They interpolated the values between the grid points by kriging (point and block) and compared with observed values. The means of observed and kriged values were at par, though the estimation variance calculated by block kriging was 6.51 times lesser than point kriging and 11 times than classical techniques. The sample size was also very less in block kriging as compared to these two techniques.
Caridad-Cancela et al. (2005) reported that different interpolation methods (kriging, conditional simulation, and inverse distance) render similar results for the spatial pattern of total Zn and Cu distributions in cultivated soils in Galicia, NW Spain. These methods were found to be useful to determine the spatial distribution and uncertainty and, thus, to characterize the Zn and Cu status in the scale examined.
2.2 Historical development of nutrient deficiency assessment
Plant analysis as a diagnostic tool has a history dating back to the early 1800s when scientists recognized the relationships between yield and the nutrient concentrations in plant dry ash. The relationships existed between crop yield and the nutrient content of’ plant ash (Liebig, 1840; Hall, 1905; Mitscherlich, 1909; Rashid, 2005; Self, 2005). Plant analysis provides information on the nutrient status of plants as a guide to nutrient management for optimal plant production, assessing the quality of produce, nutritional status of regions, nutrient levels in diets available to livestock and human nutrition, and as an indicator of environmental toxicities. The concentration or ratio of total chemical element to dry matter in plant are most widely used (Marschner, 1995; Memon et al., 2005). Range of leaf nutrient values is categorized as marginal, critical and adequate. Refinements in plant analysis as a diagnostic tool continued (Smith and Loneragan, 1997; Memon, et al., 2005).
Plant age, moisture stress, and variety affect plant nutrient content (Gosnell and Long, 1971) which should be addressed while making data interpretations. Specific leaf analysis along with soil testing is more suitable to determine balanced nutrient application where, soil analysis estimates plant available nutrients and leaf analysis nutrient uptake until the sampling time (Smith and Loneragan, 1997). Critical leaf values for various horticultural and field crops worldwide are established (Smith and Loneragan, 1997).
In sugarcane plant, the middle 300 mm section of the lamina associated with the top visible dewlap (TVD, the third leaf below the spindle) is the index tissue (Clements and Ghotb, 1968). The third leaf critical values from sugar industries (Australia, South Africa, Mauritius and Guyana) has been assembled by Reuter and Robinson, (1997) which cover the macro and secondary nutrients, and some micronutrients or trace elements.
The range of third leaf N critical values used in the different sugarcane growing countries recognize that the third leaf N declines with the crop age, and this effect is well documented (Evans, 1961; Bishop, 1965; Samuels, 1969). An innovative investigation by Gosnell and Long (1971) allowed the effects of age and season to be separated. The third leaf N values declines most markedly in the first few months of growth from a mean value of 2.70% at one month of age to a mean value of 1.85% at four months of age. The rate of decline substantially reduces starting from six months of age (a mean of 1.67% N at this stage to 1.60% N at nine months of age). Mean third leaf P, K, Ca and Mg as well decline with age but the rates of reduction are less than that of leaf N. The rate of decline in third leaf P, K and Ca concentration reduces after five months of plant age. Leaf sampling is appropriate when the crop is growing actively, and the “active growth” means that stalk elongation is greater than 20 mm day-1 (Evans, 1965). Based on this reasoning, it is recommended that plant sampling in sugarcane should be done during grand growth period (Clements, 1980).
2.3 Role of varieties in nutrient uptake
Nutrient requirement of sugarcane varieties differ with agro-climatic conditions (Davidson et al., 1996; Raghaviah and Singh, 1980; Trivedi and Saini, 1986; Suggu et al., 2010) and varieties may differ in absorption of nutrients from the same soil under the same climatic condition (Humbert, 1968; Suggu et al., 2010). Sugarcane clones vary in nutrient uptake and use efficiency which translates into variation in nutrient use efficiency of varieties (Schumann et al., 1998; Robinson et al., 2008; Chohan et al., 2010). Therefore, variety becomes a factor in nutrient uptake and critical leaf nutrient values. Significant difference among varieties was observed in Zimbabwe for third leaf N, P, K, Ca and Mg (Gosnell and Long, 1971). Third visible dewlap leaf N in the Swaziland variety NCo376 is higher than that of in NCo3l0 and NCo334 (du Randt, 1978). The varietal difference in the CSR leaf testing system known as optimum nutrient indices, existed for various Queensland varieties for N and K expressed as % dry matter, and P as the P:N ratio (Farquhar, 1965).
2.4 Nutrient assessment and interpretation techniques
2.4.1 Critical nutrient level (CNL) concept
Familiarity with the relationship between dry matter accumulation and nutrient concentration help in the interpretation of plant analysis (Memon et al., 2005). The range of leaf nutrient values has been categorized into marginal, critical and adequate for production (Clements and Ghotb, 1968). The yield is severely affected when a nutrient is deficient, and when the nutrient deficiency is corrected, growth increases more rapidly than nutrient concentration (Havlin, et al., 2004). Under severe deficiency, rapid increases in yield with added nutrient can cause a small decrease in nutrient concentration. Critical nutrient level is defined as the nutrient concentration range in the plant below which crop yield is significantly reduced. This is called Steenberg effect and results from dilution of the nutrient in the plant by the rapid plant growth. When the concentration reaches the critical range, plant yield is generally maximized. Nutrient sufficiency occurs over a wide concentration range, wherein yield is unaffected. Increase in nutrient concentration above the critical range indicate that the plant is absorbing nutrients above that needed for maximum yield. Luxury consumption is common in most plants. Plants that are severely deficient in an essential nutrient, exhibit a visual deficiency symptom. Plants that are moderately deficient exhibit no visual symptoms although yield is reduced. In luxury consumption plants continue to absorb a nutrient in excess to what is required for optimum growth. This extra consumption is without corresponding increase in growth, and with higher crop yields, a greater concentration of nutrients is required (Havlin et al., 2004).
The Critical Nutrient Level occupies the portion of the curve where the plant nutrient concentration changes from deficient to adequate; therefore, the CNC is the level of a nutrient below which crop yield, quality, or performance is unsatisfactory. Considerable variation exists in the transition zone between deficient and adequate nutrient concentrations which makes it difficult to determine an exact CNC. It is more realistic to call it as Critical Nutrient Range (CNR) (Tisdale et al., 2002; Memon et al., 2005). This concentration range lies within the transition zone, a range in concentration in which a 0 to 10 % reduction in yield occurs, with 10% reduction in yield point specified as critical value of the element (Okhi, 1987). In addition, Regional Standard Value, Critical Nutrient Range, Critical Nutrient Level, Critical Deficient Level, and Critical Toxic Level are new interpretative concepts but their use is limited (Jones et al., 1991; Memon et al., 2005).
Leaf tissue analysis values have been traditionally interpreted using the critical range approach, considering each nutrient independently. This approach has limitations, when nutrients are considered individually, values equal to or higher than the critical level are not always associated with high yield or values lower than the critical level are not always related to low yield (Dumas and Martin-Prevel, 1958), and propose the use of ratios instead of concentrations as diagnostic norms.
2.4.2 Deviation from optimum percentage (DOP)
Deviation from optimum percentage (DOP), is an alternative methodology for plant mineral analysis interpretation (Montanes et al., 1993; Mirabdulbaghi, 2014). DOP = [(C x 100)/Cref] – 100 where C is the nutrient concentration in the sample to assess and Cref is the optimal nutrient concentration used as a reference value. DOP zero is an optimum nutritional situation for any element, and the third leaf critical value for all varieties grown as winter-cut irrigated cane is 0.85% if samples are collected during mid-October to November. This value increases to 0.95% K for December and January sampling and to the established value of 1.05% for samples collected in February to April (Donaldson et al.,1990).
DOP are ordered for a sample with increasing positive indexes (or excess) and increasing negative indexes (deficit) similar to that obtained with the DR1S method (discussed next).
2.4.3 Diagnosis and recommendation integrated system (DRIS)
DRIS was formulated and described by Beaufils (1971 and 1973). Walworth and Sumner (1987) and Beverly (1991) and Srivastava (2012) presented reviews on DRIS which has been applied to several plant species including forage, fruit, nut and vegetable crops, and forest trees. DRIS ranks the nutrient order of requirements among the elements analyzed. The DRIS approach offers additional advantages over the critical value and sufficiency range methods for assessing yield and/or growth responses to fertilizer inputs (Walworth et al., 1987; Sumner, 1979; Beverly et al., 1984). Jones et al. (1991), however, reported no advantages of the DRIS method over the more traditional techniques. Possibilities of comparative interpretation of DRIS and DOP methodology have been tried (Montanes et al., 1991; Srivastava, 2012).
The CNL concept has limitations i.e. (i) it would not define whether the deficiency is acute or not, and (ii) nor it would identify which nutrient is the most limiting when more than one nutrients are classified as deficient (Baldock and Schulte, 1996). In addition, nutrient tissue contents are influenced by dilution or concentration effects caused by variations in the dry matter yield (Jarrel and Beverly, 1981). The DRIS method is based on the comparison of dual relationships (N/P, P/K, K/Ca, Ca/Mg, etc.) in samples with standard or norms values (Beaufils, 1973). The DRIS method is an alternative to the interpretation of results of leaf analysis, because the method allows the calculation of indexes for each nutrient, using its relations with others and comparing them with a population reference (Beaufils, 1973), instead of the absolute and isolated concentration from each one. The DRIS index is the average of the deviations of relationships containing a nutrient in relation to their optimal values. Each relationship between nutrients in the population of high productivity is a DRIS norm and has their respective mean and standard deviation. The index of nutrients in a sample can vary from positive to negative, but the sum of these indexes will always be equal to zero. The sum of the absolute values of these indexes is the nutrient balance index (NBI), expressing the nutritional balance of the crop sampled. Lower NBI represents a lower nutrient imbalance (Hernandes et al., 2014 and Barloga, 2014).
2.5 Theoretical basis of DRIS and its implementation
DRIS relates to the nutrient contents in dual ratios (N/P, P/N, N/K, K/N…), because of the relation between two nutrients, the problem of the biomass accumulation and reduction of the nutrient concentration in plants with its age is solved (Beaufils, 1973; Walworth and Sumner, 1987; Singh et al., 2000; McCray et al., 2010). First, the standards or norms are established in DRIS as it applies for the diagnostic methods. The standards or norms are obtained from high yielding population, named reference population, selected from a larger population. The selection of the reference population is an important factor for the DRIS effectiveness and success (Walworth and Sumner, 1987; Mourao Filho, 2004; McCray et al., 2011). The criterion to separate two sub-populations is arbitrarily chosen, and each sub-population ought to present normal distribution. Letzsch and Sumner (1984) proposed 10% high yielder of the population. The reference population may consist of observations with yield not less than 80% of the maximum yield (Malavolta and Malavolta, 1989).
The largest variance ratio between high and low yielding populations is among the several criteria used to select the best adequate expression (Letzsch, 1985; Walworth and Sumner, 1987; McCray et al., 2011). That same criterion was named “F value” (Nick, 1998). Use of direct and indirect ratios, have been evaluated as the ratio order which can interfere in the indexes (Bataglia and Santos, 1990). Nick (1998) suggested the criterion named “r value” for the nutrient ratio order choice for DRIS application in pruned coffee plants. The “r value” is referred to the correlation coefficient between plant yield (or any other response values) and a nutrient pair ratio, once in direct and then in inverse order. In citrus “r value” is an adequate criterion for the determination of the nutrient ratio order (Mourao Filho et al., 2002).
Revisions have been proposed in the original method of Beaufils (1973) to increase accuracy in the nutritional diagnosis through nutrient ratio functions (“F value”) by Jones (1981) and Elwali and Gascho (1984). The Beaufils (1973) and Elwali and Gascho (1984) procedures had similar results, and Jones (1981) procedure showed dependence on the nutrient ratio (Batagalia and Santos, 1990). The original DRIS method uses only the nutrient ratio functions in the second and last stage calculation of DRIS indices (Beaufils, 1973). Whereas, the M-DRIS method includes dry matter (O, H and C) and is treated as one of the nutrient in the indices calculation (Hallmark et al., 1987; Walworth et al., 1986; McCray et al., 2013).
Nutrient index is composed of functions (“F value”) where all the individual nutrient pair ratios are summed up and divided by the number of functions (Mourao Filho, 2004). The value f(A/B) is added to “A” nutrient index and subtracted from “B” nutrient index. Sum of all the nutrient indexes is around zero (Walworth and Sumner, 1987; McCray et al., 2013). Consequently, the sum of the nutritional indexes must be zero. A negative (lower than zero) nutrient index means deficiency (more negative means higher deficiency), and high index values (the more positive) indicates excessive quantity of the nutrient.
2.6 Application of DRIS in crops and fruits
Successful DRIS use as diagnosis methods has been reported for several horticultural, ornamental and fruit crops; i.e. tomato (Hartz et al., 1998; Rheem Abd El et al., 2015), soybeans (Hallmark et al., 1989, 1990) (M-DRIS), maize (Creste et al., 2001), eucalyptus (Wadt, 1996; Wadt et al., 1998), pineapple (Agbangba et al., 2011), orange (Hernandes et al.,2014), lettuce (Hartz and Johnstone, 2007), wheat (Akhtar, 2012), Aonla (Nayak et al., 2011), hybrid poplar (Meyer, 1981; Kim and Leech, 1986; Walworth et al., 1986; Reis Jr. and Monnerat, 2002; Reis Jr. and Monnerat, 2003; McCray et al., 2010) and sugarcane (Beaufils and Sumner, 1976; Walworth et al., 1987; Elwali and Gascho, 1994; Morris et al., 2005; Galindeze et al., 2009; McCray et al., 2010; McCray et al., 2011; McCray et al., 2013).
DRIS norms were determined for vineyards in Germany (Schaller and Lohnertz, 1984; Hochmuth, 2010) based on 7000 leaf analysis and sugar content in the fruit. The developed norms allowed the detection of limiting nutrient concentrations for productivity and quality, which could not be detected otherwise although relationship between soil analysis and DRIS norms was very poor. DRIS norms were also derived for grapes in India and evaluated in a low yielding vineyard consisting of only 48 plots (Chelvan et al., 1984). A new criterion was developed to classify the N status of grapevine cultivars based on the DRIS indexes calculated with soil and leaf analysis data (Bhargava and Raghupathi, 1995) in another research in India.
DRIS method was used to identify mineral deficiencies in mango in USA (Schaffer et al., 1988) pointed Mn and/or Fe concentrations below the critical value in two of the three decline-affected mango orchards. Szucs et al. (1990) investigated the DRIS norms for apple orchards in Hungary where the data consisted of yield and leaf nutrient concentration from 18 orchards collected for three consecutive years. DRIS indicated K-excess and P-deficiency, while the N concentrations were adequate. Another study for apple orchards, involving only macronutrients established imbalances referred to the N-excess and Ca-deficiencies was carried out in New Zealand by Goh and Malakouti (1992). They reported that the best sampling period for this type of study was between 3-5 months after blooming. For pecan DRIS norms were obtained from 3000 entries of yield and 11 nutrient concentrations, and reference population was selected from 25% best yielding plants (Beverly and Worley, 1992). DRIS norms for banana, based on 915 observations data with reference to sub-population ≥70 t ha-1 yielder reported that the method of critical values and the DRIS norms methods were similar except for K and K/nutrient ratios (Angeles et al., 1993). A similar study carried out in Tanzania also derived new norms for banana plantations using both DRIS and the critical value methods (Wortmann et al., 1994). Gregoire and Fisherb (2004) investigated the relative benefits of vector analysis, DRIS and critical level approaches for diagnosing nutrient deficiencies for loblolly pine (Pinus taeda L.). The study revealed that the diagnostic efficacies differed in predicting the response to fertilization, and no method alone was accurate enough for precisely predicting the response across soil groups.
DRIS norms were developed for tomato in Nile Delta of Egypt using N, P, K, Fe, Zn, Mn and Cu concentration leaf nutrient and fruit yield divided into high-yielding (≥22.0 t ha-1) (Rheem Abd El et al., 2015).
DRIS norms were established for pineapple plantations Benin using the N, P, K, Ca, Mg and Zn nutrient concentrations in leaf. The fruit yield data was divided into high-yielding (>66.7 t ha-1) and low-yielding (<66.7 t ha-1) sub-populations, and the presented norms were significantly different from those presented in the literature, except for N/K whose value was similar to the existing norm (Agbangba et al., 2011). Hernandes et al. (2014) derived critical levels and nutrient sufficiency ranges in the leaf tissue for Pera orange.
Akhtar (2012) compared critical level approach and DRIS for diagnosing nutrient deficiency in wheat in Hyderabad district, Pakistan. DRIS norms are similar at two growth stages studied in wheat. Nitrogen was indicated in short supply and Mn and B in excessive concentration and recommended DRIS for developing recommendations for fertilizer application in the region. Soltanpour et al. (1995) compared DRIS with the nutrient sufficiency range (NSR) for corn (Zea mays L.). Although DR1S is potential for interpreting plant nutrient composition, the following flaws in DRIS were reported: (i) very high levels of one nutrient can cause false relative deficiency diagnosis of other nutrients, and (ii) an optimal ratio between two nutrients produces maximum yields only when both nutrients are in their respective sufficiency ranges. They recommended using the NSR technique in combination with a soil test to avoid the mis-diagnosis of Zn and Cu deficiencies in corn when N is extremely deficient.
Galantini et al. (2000) conducted a study to formulate the initial values of DRIS norms for artichoke (Cynara scolymus L.) in the southeast Spain using DRIS. Samples were collected from three best plots for every fifteen days for three months in a row, and for a total of 108 samples. Thereafter, the DRIS norms were developed for the elements N, P, K, Ca, Mg, Zn, Fe, Cu, Mn and for all their mutual relations through the respective statistical analysis.
The use of critical approach for determining the N, P, K and S status of perennial ryegrass (Lolium perenne) swards had disadvantages, as the nutrients in plant tissue vary with crop age and with the concentrations of other nutrients in plant (Bailey, 1997). Therefore, he recommended the use of DRIS, because it is based on relative rather than absolute concentrations of nutrients in plant tissue. He specified, that without an internal reference, plant growth could be limited by multiple nutrient deficiencies even if N, P, K and S indices are all close to, or equal, to zero (i.e. the optimum), simply because the absolute concentrations of each nutrient (while low) are in the correct state of balance.
Teixeira et al. (2009) conducted a study to form the preliminary DRIS norms and critical leaf nutrient level (CLN) for the “Smooth Cayenne” pineapple plantations of Sao Paulo, Brazil. To develop the DRIS norms, they created a data base of leaf nutrient concentrations (N, P, K, Ca and Mg) and fruit yields for 104 samples. They divided the data into high-yielding (>65 t ha-1) and low-yielding (<65 t ha-1) sub-populations, and norms were computed using standard DRIS procedures.
Meyer (1981) conducted a study on sugarcane yield data and third leaf analysis from 96 fertilizer trials to establish whether DRlS can be used to improve the quality of the fertilizer advisory service. He reported that in general predictions of a yield response to applied N, P and K, DRIS was more reliable when the nutrient threshold approach was used at an early stage of crop development. The results of his experiments show that imbalances of N, P and K can be detected four to six weeks earlier by using DRlS than by the threshold approach. He further reported that DRlS can be used fairly reliably to indicate N, P and K deficiencies in order of decreasing importance.
Meyer (1987) tried to improve the quality of the fertilizer advisory service by DRlS using the third leaf analysis from 96 fertilizer trials. Predictions of a yield response to applied N, P and K were more reliable with DRlS than with the nutrient threshold approach which was used at an early rather than a late stage of crop development. Further, N, P and K can be detected four to six weeks earlier by using DRlS than can be accomplished by using the threshold approach. DRlS can be used fairly reliably to indicate N, P and K deficiencies in order of decreasing importance. Reis Jr. and Monnerat (2002) established DRIS norms for sugarcane crop using mean yield and foliar of low- and high-yielding groups. Leaf were analyzed for N, P, K, Ca, Mg, S, Cu, Mn and Zn content of 126 commercial sugarcane fields in Rio de Janeiro State, Brazil. Nearly all nutrient ratios showed statistical differences between mean of the low- and high-yielding groups. The DRIS norms for micronutrients with high S²l /S²h ratio and low coefficient of variation were found to provide more security to evaluate the micronutrient status of sugarcane. Effectiveness of a summer fertilizer supplement was determined through DRIS. A more cost-effective use of leaf analysis appears to be with the adjustment of the next amendment or fertilizer application, generally for next year’s crop or at the next sugarcane planting, rather than adding an additional fertilizer supplement to the current crop (Meyer et al., 2008; McCray et al., 2010).
Miles et al. (2010) emphasized that restricted growth arising from a severe deficiency of one particular nutrient may result in deficiencies of other nutrients being masked in the leaf concentration data, and the interactions between nutrients for uptake by plants may markedly impact on the diagnostic process. In the case of sugarcane, N x K and N x S interactions are of particular significance, with seasonal variations in K uptake adding to the difficulties associated with the interpretation of leaf K data.
Reis Jr. and Monnerat (2002) compared three DRIS norms of sugarcane crop, and reported that the means for several nutrient ratios were significantly different for these three DRIS norms. The DRIS norms were not universally applicable to the sugarcane crop, therefore, locally calibrated DRIS norms should be developed, and norms developed under one set of conditions should only be applied to another if the nutrient concentrations of high-yielding plants from the different set of conditions are similar. Similar work was carried out by Abd El-Rheem et al. (2015).
SUMMARY AND CONCLUSIONS
Nutrient imbalance as a limiting factor in sugarcane yield was evaluated and interpreted using Critical Nutrient Level (CNL) and Diagnosis and Recommendation Integrated System (DRIS) approaches. One hundred twenty three sugarcane farms between 24°0’ to 25°27’ N and 67° 35’ to 68°45’E in lower Sindh, Pakistan were assessed through soil, associated plant index tissue and sugarcane yield analysis. The soils were calcareous with pH 7.7 to 8.7, low in soil test nitrogen, low to medium in extractable P, and adequate in extractable potassium. Plant available zinc was low, boron was medium and copper and iron for all the soils were high. Selected soil nutrients were found spatially variable. The soil zinc was lower in Mirpur Sakro and Thatta sub districts (Talukas) and high soil zinc was towards Sujawal-Jati sub districts. Similar spatial pattern existed for plant available iron, potassium, and boron which was related with soil type; and the land capability map further helped to understand the spatial variation in the nutrient status in the sugarcane growing area.
Plant tissue nutrients differed significantly (p > 0.01) with the soil type except for nitrogen and phosphorus. Nitrogen and phosphorus in plant index tissue were slightly below the critical value and the optimum range, while, potassium was above the critical value and higher than the optimum range. Zinc, boron, manganese, iron and copper were in sufficiency range in the plant tissue.
Variety is a factor that appears to affect nutrient acquisition and consequently plant nutrient values. Similarly, soil type appeared to affect the nutrient accumulation. Several examples suggested that edaphic factors influenced nutrient levels in plant as plant tissue nutrients content in the varieties also changed with the soil type.
The yield varied extensively, thus instead of the nutrient status in absolute terms, nutrient ratios may be limiting the maximum potential yield. The high yielding population had generally greater nitrogen, phosphorus, potassium and manganese than the low yielding varieties. The magnitude of difference for zinc and boron was far greater between low and high yielding populations. Copper and iron concentration difference between high and low yielding populations was negligible. Opposite to nitrogen to potassium ratio, the low yielding populations in each variety had wider nitrogen to phosphorus ratio than the high yielding population of corresponding variety. Ratio of nitrogen with zinc, boron, copper and manganese was wider in low yielding population, suggesting excess nitrogen than the micronutrients. Nutrient ratios show statistical differences between mean values of the low- and high-yielding groups. Low P than Cu in low yielding than in the high yielding population, and high potassium than zinc, boron, and copper in low yielding population compared to high yielding population, suggested the low levels of micronutrients as constraint to high yields.
Comparative ratio of ratios of nutrients in low and high yielding populations indicated deficiency of these nutrients more accurately. The DRIS index for nitrogen between 2.96 to 4.51 indicated that nitrogen was sufficient in the leaf tissue at the current N application rate, therefore, the current application rate be maintained. The phosphorus index was -6.23 to 3.58, and indicated deficiency, thus indicating the need for additional phosphorus application in case Thatta-10. The index for potassium was 2.57 to 8.10 which indicated high level of potassium in the sugarcane plant tissue. The index of zinc was between -12.23 to -8.93, and the magnitude of difference from zero of balance nutrition showed the severity of deficiency suggesting potential for response likely to be high to the application of zinc. The index for boron ranged between -14.87 (deficient) to -0.26 (adequate) for the four varieties. It was adequate only in case of Thatta-10 with high probability of response to boron application. The average indices for copper and iron indicated high status of these nutrients in the sugarcane plant tissue. Manganese index was -3.12 to -8.20, and indicated deficiency in Thatta-10. Different magnitude of indices of nutrient varying with the varieties indicated a variable nutrient imbalance. The phosphorus is adequate in all other varieties while it is deficient in Thatta-10, potassium is high in BL-4 and Triton while adequate in group of “other” varieties and Thatta-10. Boron is adequate in Thatta-10 while deficient in BL-4, the group of “other” varieties and Triton. Manganese is deficient in Thatta-10 while adequate in all other varieties.
The study provides guidelines for sugarcane nutrition on a regional level. Large commercial growers and policy makers can benefit from the findings. Similar, diagnostic studies should be carried out for other sugarcane growing regions.
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