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Sugarcane as a Source of Biofuel

Chapter 1: Introduction



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)(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.

2.1.1 Semi-variograms

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(xh)]2                   [1]

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           [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).



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.



Research question

Chapter 2: Literature review

  • Biofuel related literature
    • Ethanol production process
    • Feedstock review
    • Biofuel production cost
    • Advantages and benefits
  • Sugar production related
    • Production process
    • Sugarcane as feedstock
    • Residue and its recycling
    • Costs and prices
  • Literature on trade-off between sugar production and biofuel production

Chapter 3: methodology

Approach to trade-off analysis

Data and inputs

Identification of alternative cases for analysis

Chapter 6: results and discussions

Conclusion and recommendation for further work


Chapter 1: Introduction

During last couple of years, in serious consideration for the worldwide economy and the environmental pollution that burning fossil fuels causes, there has been an increased interest in the production of renewable energy. Gasoline provides the single biggest percentage of the world’s energy and the total world consumption in 2015 was 95 million barrels per day. [1] Burning gasoline is responsible for a high percentage of carbon dioxide emitted to the atmosphere. In Brazil, most of the gasoline produced is used in the transportation of vehicles which leads to pressure being put on the supply of gasoline even further. Last year alone the global oil consumption grew by almost 2 million barrels per day, which was considerably more than the 1.1 million barrels used in 2014.  [2]

An essential stride is in effort, to take care of the issue to replace the fossil fuel energy with biofuels.  Fossil fuels and greenhouse emission can be greatly reduced due to the usage of biofuels in the transportation sector. [3] In the transportation sector you would present the idea of introducing transportation vehicles, which can run on biofuels or use biofuels which have a certain amount of gasoline to run the vehicles. The two possibilities presented are not totally unrelated. In any case, mixing biofuels with gasoline based fuels for the current transportation vehicles has benefits that even the usage of low mixture of biofuel with gasoline, will bring about considerable volumes of fuel being substituted by biofuels. [4]

Biomass is a renewable energy which can be produced by certain types of crops such as sugar cane, sorghum and corn starch. It is made of many complex carbohydrates which were recently living organisms that cannot be used for food or feedstock’s and hence are called lignocellulosic biomass. To break down the mixture of complex carbohydrates which are cellulose, hemicellulose and lignin and produce ethanol from them, a pre-treatment is needed to reduce the size of the biomass. This ensures that the hemicellulose is broken down into sugars which at that point causes the structure of the cellulose component to be opened up. The portion which has just been opened up is hydrolysed by protein enzymes and thus causing the fermentation into ethanol. Similarly, the hemicellulose is also fermented into ethanol. The remaining waste which is the lignin is consequently burned to produce fuel which in turn powers the entire process. [5]

Likewise, another form of source which biomass can be created from is forest residues such as dead trees or fallen branches. These pieces of organic waste, would be either burnt in forest fires or dumped which can be used to optimise in the manufacture of biomass energy. [6]

Sugarcane is a multipurpose harvest whose segments might be utilized in aiding sugar generation, for different energy bearers or finished results (power, fluid biofuels and heat) which upgrades its financial potential. For a long time, plant reproducers and agronomists have concentrated on expanding sucrose yields per hectare and mill operators on expanding recoverable sucrose per ton of sugarcane in sugar factories. Therefore, to fully capitalise on the energy potential of sugarcane you would need to go for more general approach concentrating of different production methods [7].

Chapter 2: Literature review

Throughout this report we will be looking at the costs and investments required to produce the two and consider the revenues generated from each stream to find out what combination of the output should be used to maximise profit for business in Brazil. We will also be looking at when biofuel becomes a viable output instead of sugar production. However, USA will be widely used for comparison because of its similarity in size and production methods. The reason why Brazil was chosen for this report is discussed below.

There are multiple countries where ethanol is widely produced. The United states of America is one of the main producers of ethanol and their ethanol production has eclipsed all other nations. They also have highly extensive amount of resources which enable to produce so much ethanol. As of 2015, the amount of fuel ethanol produced in USA peaked at approximately 14,800 (million gallons). the nearest country which produced the most fuel ethanol was brazil and it had produced less than half of what USA produced at 7,090 (million gallons). [8 FIGURES???] In the USA the main feedstock product used to produce ethanol is corn. Producing ethanol from corn is much more difficult than using a sugar based feedstock such as sugar cane. This is because converting the sugar to ethanol involves only a fermentation stage whilst on the other hand corn requires the addition of several different enzymes and needs more cooking to convert it into ethanol.

One of the main reasons why the USA produces corn based ethanol is because corn production plays a big part in the USA’s economy. This is a very significant since both countries are quite similar in size. The amount of arable land available in USA could play a big part of why more ethanol is produced in USA than Brazil as they are roughly the same size. The United States of America has approximately 3 times as much arable land as Brazil around 1,650,00 km2 whilst brazil is only around 586,000 km2. [9 NEED TO REMEMBER WHERE FIGURES CAME FROM]

On the other hand, Brazil is a nation which over the years in which their ethanol production has increased consistently for the last ten years. This meant that Brazil became the number two country in the world in terms of ethanol production.  There are many favourable conditions which make Brazil a country where ethanol is produced such as tradition on culturing sugarcane, also sugar cane being the most efficient raw material required for the ethanol production and finally the climate. There are several reason why Brazil initially decided to produce Ethanol to begin with. The first reason would be that in the 1970s, 1 litre of ethanol was worth three time more than a litre of gasoline which meant that most countries didn’t think about investing in biofuel production. This is all changed for Brazil when they decided to produce ethanol from sugar cane. This was because of the low cost of the sugar at the time and the ability of the countries familiarity with the feedstock. [10]

The second reason was that in the year 1979 an oil crisis occurred globally which further made Brazil go into producing a gasoline substitute which was ethanol. A national alcohol program which was known as PROALCOOL was made which aimed at reducing the countries reliance on important oil or even oil-based automotive fuel. [11] A number of factors helped enable the governments influence to start producing ethanol which the main point would be that at that time, ethanol’s price was lower than gasolines. Also, because that gas stations would be obliged to sell ethanol as oil the oil crisis continued. The main aim of the PROALCOOL was to increase the production of sugar based ethanol from sugar cane. This guaranteed purchases by the governments to farmers for growing the sugar cane. Another key point which was embedded in the PROALCOOL was that the ethanol produced would sell for a price of just 59% of gasoline at the gas station. [12]

This also helped the producers of sugar acne as the they were guaranteed to be paid for the growth and cultivation of the feedstock.

There are still a number of countries which could be taken into consideration. For instance, China which recently started to consistently produce ethanol could be one. The reason for them producing ethanol came from a plan which was put into place by the Chinese government called the 12th five-year plan. Some of the targets of the plan include reducing fossil fuel consumption and to also promoting low-carbon energy sources. [13] There are a number of concerns for china in terms of hazardous air pollutions and some concerns about the impacts of climate change. Therefore, there a number of efforts on the sustainability development of the country. However, as there was a quick growth of the number of feedstock’s being used for ethanol production there were a number of concerns raised. The one major concern was that the possible issues of food shortages which had swept China in many regions where ethanol production was occurring. [14]

Another set of countries where ethanol production is slowly on the increase is the continent of Europe. In 2014, the production capacity of Europe was close to 7.8 billion litres which was over 90% of the total production capacity which stood at 8.8 billion litres. However, the actual production rate was at 6.6 billion litres of ethanol compared to 2013 which was an increase of approximately 13% in production. [15] The feedstock used to produce ethanol in Europe is corn which is similar to USA as they primarily use the same feedstock. However, Europe has suffered due to the fact that European producers are under growing pressure from a mixture low market prices and also the volatility in the Europe’s biofuel policy.

Figure 1: World Ethanol Production [16 EXCEL FILE GOT THAT SAVED]

This table shows the world fuel ethanol production worldwide but only in the main countries associated with ethanol production. From the table, USA is the leading country in ethanol production and it also shows that the quantity of the ethanol produced never decreases as the years go by. Brazil on the other hand is also increasing as the years go by but during the years 2011 and 2012 it began to decrease. This was due to the face that brazil had a poor harvest of sugar cane during them years which led them to importing more ethanol.  However, sugar cane is the primary feedstock in ethanol production in brazil and it’s the reason why brazil was the country of choice for this project.

Ethanol production process

Brazil is the world’s largest sugarcane ethanol producer and a world pioneer in using ethanol as a motor fuel. Within the year 2015/2016 the production of ethanol was approximately 30.23 billion litres. This ethanol is sold in the market as either pure fuel or combined with gasoline. In Brazil alone the gasoline has a blend percentage between 18 to 28 percent ethanol. [17] The total sugar cane production in Brazil was 666,824 (thousand tons) in the 2015/2016 harvest season, compared to the 30,232 (thousand m3) produced. This value is very high almost double compared to the harvest which occurred the year before which only produced 17,636 (thousand m3).

There are 3 main stages which make up the production process of ethanol. These are

  • conversion of biomass to fermentable sugars;
  • fermentation of sugars to ethanol; and
  • separation and purification of the ethanol


Two noteworthy parts of plants, starch and cellulose, are both comprised of sugars, and can on a basic level be changed over to sugars for fermentation. The first step would be the breakdown of complex carbohydrates into simpler ones. Ethanol is created by microbial maturation of the sugar. Microbial maturation will right now just work straightforwardly with sugars. The aging is exothermic; in this manner, cooling is expected to hold the response under maturation conditions. Yeast is included alongside supplements (nitrogen and trace elements) to keep yeast developing. Maturation can occur in both group and persistent reactors; however, Brazil essentially utilises consistent reactors. [18]

Ethanol can be delivered from biomass by the hydrolysis and sugar maturation forms. To deliver sugars from the biomass, the biomass is pre-treated with acids or proteins so as to diminish the measure of the feedstock and to open up the plant structure. The cellulose and the hemi cellulose parts are separated (hydrolysed) by catalysts or weakened acids into sucrose sugar so that it is then aged into ethanol. The lignin which is additionally present in the biomass is ordinarily utilized as a fuel for the ethanol creation plants boilers. Before the fermentation stage certain crops need saccharification. Saccharification enzymes aid in the breakdown of the complex carbohydrates such as starch into plain sugars.

Ethanol can either be produced using a dry mill process or a wet mill process. It’s usually produced by means of a fermentation process which uses glucose resulting from sugar based food stocks such as sugar cane, molasses or sugar beet. The fermentation can also occur without using sugar based food stocks and instead using either starch based food stocks or cellulose based food stocks.















Timeline of Ethanol

American inventor Samuel Morey was the first man to develop an engine which could be run using ethanol in 1826. He first noticed its efficiency when fuel lamps which were originally powered by oils such as vegetable oil ended up becoming too expensive to purchase. Morey received a patent for an internal combustion engine which was his first ever.  Some 35 years later, another inventor who went by the name of Nikolaus Otto who was of German decent, was fascinated in work by Morey and therefore dedicated his life to it. He built the first ever four stroke engine which is more commonly known as four cycle. The four cycle is an internal-combustion engine in which the piston completes four different stroke while turning a crankshaft.

In the year 1896 the first ever automobile was built which was completely powered by 100% ethanol.  American industrialist Henry Ford, who was the founder of the Ford Motor company, used biofuels to power his first vehicle which was the Quadricycle. Up until modern times Ford Motor company still produces Flexible-Fuel vehicles (FFVs).  12 years later in the year 1908, Ford developed their first ever commercially produced flexible-fuel vehicle. The car named Model-T had an adjustable carburettor, low compression engine and also a spark advance. This enabled it for the car to switch forth and back from ethanol to gasoline. In recent times the company has modified two of their most popular models – the Ford Focus and the Ford C-MAX and modified in a way in which they are able to run on biofuel.,,2139914,00.html

One thing in history which had a major effect on ethanol production was World War 1. The fuel consumption increased in the US to almost 60 million gallons per year. During this period not only did the use of ethanol increase but it was also used to for war purposes as well. They did by adding a denaturant to the ethanol to make it poisonous for human consumption. In the year 1919, Prohibition was bought in to effect. This was a movement driven by religious groups which considered alcohol a threat to the nation. The 18th Amendment prohibited the transportation, manufacturing the sale of intoxicating liquors. However, Prohibition proved very difficult to implement as it led to an increase in organised crime which dedicated itself to produce and smuggling of alcohol.

Having started out by lighting fuel lamps to eventually powering Quadricycles to finally being able to power aeroplanes. Pilot Chuck Yeager was the first pilot to have exceeded the speed of sound at Mach 1.07 over the Mojave Desert in California. The aircraft which was a Bell X-1 airplane contained a blend of ethanol and liquid oxygen as fuel which amounted to almost 600 gallons. A major crisis occurred in 1964 at the Indianapolis 500 race when more than 150 gallons of gasoline was set ablaze which claimed the lives of several drivers. This prompted the organisation to ban gasoline powered cars and switch to methanol.

Towards the end of world war 2 and many years after that, there was a reduced demand for fuel and therefore the price of fuel dropped. This subsequently led to ethanol being cast aside for a period of time since people didn’t need to depend on ethanol anymore. During the 1980s the united states of America produced the Energy Security Act, this helped give loans out to producers of ethanol so that the production levels could rival the level it was at the time period of 1950-1970.

A phenomenon known as engine knock occurs in a vehicle when the fuel used is at a low octane level at which the car can operate on. A type of lead known as tetraethyl lead was added to vehicles so that it would help reduce the knocking effect which occurs in the engine. It also helps increase the octane ratings of the fuel so that the knocking effect doesn’t take place. Nevertheless, this type of lead was ultimately removed from the fuel due numerous health reasons. One of the crucial reasons being that the inhalation of the lead particles from the exhaust lead to the impairment of the nervous system of children. This type of lead in the fuel could also lower a child’s intelligence level and cause brain damage in not only children but also in adults.













































A biofuel is a type of renewable fuel which can be attained through a procedure called biological carbon fixation. This process occurs when inorganic carbon is converted into organic compounds. As a matter a fact any hydrocarbon fuel which takes anything from a period of time whether it be a couple of days, weeks or even months and was originally produced from organic matter is considered a biofuel. There are three main categories in which biofuels fall under. They are first generation biofuels, second generation biofuels and third generation biofuels. First generation biofuels are when biofuels are produced directly from the food crop source. This could either be sugar cane or corn as these are two of the world’s primary source for ethanol fuel. The first generation fuel has an advantage over the other two generation fuels as they have the benefit of reducing the overall greenhouse gas emissions. This because the carbon absorbed by the plants during their growth equates with the carbon which is released into the atmosphere when the biofuels are burned.

Advantages of corn

  • Able to use the entire stalk to produce ethanol
  • Simple to convert it from corn starch to ethanol
  • No extra lands cost due to farming corn

Disadvantages of corn

  • Energy yield only produces about 20% net yield
  • The production rate of corn is very low as it only produces around about 350 gallons of fuel per acre

The second generation biofuels are when the feedstock which is needed to serve as the food source doesn’t really have to be food crops. It deals with the microbial fermentation of non-food crops. They are usually waste material such as used vegetable oil. When the vegetable oil has served its food purpose and it is no longer fit for human ingesting then it would be used as a food source for the second generation biofuels. For it to qualify as a second generation biofuel food source it must not qualify for human consumption. Common types of secondary generation feedstock’s include switch grass, wheat straw and waste vegetable oil (WVO).

Advantages of grasses

  • Very high energy yield of around 540%
  • Very low fertilizer is needed for it to grow
  • Grow very fast and can be harvested multiple types during the year

Disadvantages of grasses

  • They don’t grow on unfertile land
  • Needs a lot of processing for it to be converted into ethanol

Advantages of waste vegetable oil

  • It does not affect the food chain
  • Usually readily available for it to be converted into ethanol

Disadvantages of waste vegetable oil

  • It can have an effect in the engine if it is nog properly refined

The last type is third generation biofuels and this is when biofuels are solely derived from algae. Algae is also in the second generation biofuel category but since it has higher yields with lower resource inputs than the other feedstock’s mentioned in the second generation category. This was the reason it was moved into its own category and therefore was made into a third generation biofuel. Algae is able to produce an oil which can be refined into different components of fuel.  It also can be genetically modified to produce different biofuels such as ethanol and butanol.

List of fuels that can be made using algae

  • Ethanol
  • Butanol
  • Biodiesel
  • Jet fuel

Use of ethanol







Initially when the oil crisis occurred in the 1970s which was due to a number of problems that was occurring in the middle east. This caused the price of oil to spark and thus triggered the 1970s oil crisis. To overcome this, the idea of producing gasoline which contained a small percentage of ethanol was introduced. Most popular blends of this fuel was E10 or E20 which contained 10% or 20% ethanol respectively.

At first methanol was viewed as the original alcohol to be added to the gasoline. This was because methanol can be created by gas at no extraordinary cost and the face that it is very simple to mix with gas and therefore was the most viable option. In any case when utilising methanol as a part of practice it turned out that there were safety measures which must be grasped when taking care of it, and that methanol has a hostile reaction to some of the plastic segments and even to some of the metals in the fuel framework. What we can take from this is that different materials which were new must be utilized as a part of the fuel arrangement of the vehicles and also in the circulation framework. This brought the interests for delivering a substitute fuel based on biomass has also been a major factor in the early choice between methanol and ethanol. [4]

On the 13th of October 2010 the Environmental Protection Agency which is based in the US established the E10 rule dictates that the maximum mixture for ethanol is 10% with the remainder being gasoline.  However, this lead to many problems with car manufacturers because they have had to recall cars. Toyota being one of the car manufactures to have done so have recalled 2.9 million vehicles worldwide, with 72,885 vehicles in the UK alone. However, some of the reasons for is that the engines were at risk of corroding due to the imbalance of the ethanol mixed fuel. Another possible reason was the vehicles are usually equipped with an evaporative fuel emissions control unit which is known as a canister. There were problems with the canister such possible cracks developing which after some time grows bigger and, in the long run fuel may spill from these cracks which ultimately leads to loss of fuel. The evaporative fuel emissions control unit is an important system which is designed to store get rid of fuel vapours before they can into the atmosphere. Since Toyota vehicles had possible cracks in this unit it leads to evaporative emissions. Not only does the emissions have an impact on the environment due to greenhouse gases released, it also has an impact on personal health. One major impact on health is that it affects breathing capabilities and lung capacity as it blocks the amount of oxygen that red blood cells are able to transport around the body.

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The new rule of E10 gasoline which was passed by the Environmental Protection Agency (US), has a number of advantages and disadvantages. Arguably one of the main advantage is that E10 was designed to reduce the amount of harmful emissions which get released to the atmosphere. This rule can reduce harmful emissions by up to 30%. The American medical association (AMA) believes that there is hard evidence supporting the facts that by adding ethanol to petrol and biodiesel to diesel than there is a good chance of reducing the deaths and illnesses caused by the emissions released into the atmosphere as a result of the combustion of those fuels. Another main advantage of the E10 fuel is that farmers benefit from this blend as the ethanol produced comes from the food crops that farmers grow. This would have a positive effect globally on countries which produce ethanol by adding value to agricultural crops. Another effect this would have is bring employment opportunities to the region in which the crops are produced in.


When considering biofuel costs there are many different processes to bear in mind. Costs of producing biofuels primarily depends on lands costs and labour, feedstock and how the oil market fares. Another thing it depends on the agricultural subsidies which a government aid that is usually paid to farmers. The amount of money which is paid to these farmers depend on the market prices and affects crops such as wheat, sugar cane, soy beans and rice.

One of the first things to bear in mind is the initial cost to plant the sugar cane. This depends on how big the land used to plant the food crop stretches out for. Also another cost aspect to be taken into consideration is if the land is fertile enough for the sugar cane to grow on. If this is not the case, then cultivation of the land would need to take place which adds to the cost.

When at this stage and the initial costs are stated, you are enter the conversion steps. This is the necessary steps needed to convert the sugar cane into ethanol. This proces

Advantages of Bioethanol

Biofuels are unlike their energy counterpart’s fossil fuels as they a renewable energy source.

Since they are acquired from products that are reaped annually or in the case of algae which is acquired monthly, the resources needed to make the biofuels are unrestricted.  Fossil fuels on the other hand is considered non-renewable as they take millions of years. Biofuels are considered cost effective as the price in the oil market is similar to that in fuel. This means it takes less money for them to make it compared to standard fuel which is obtained via crude oil. They are also considered much cleaner fuels as they have very less carbon emissions which get released to the atmosphere compared to their fossil fuel counterparts. Another fact which could be argued is that the amount of carbon dioxide which is absorbed by the plants in the first place during the process of photosynthesis, equals roughly the same amount of carbon dioxide which is released during the production process.

It impacts the economy in a positive way since the need to acquire fuel from foreign countries is greatly reduced. By doing so, you also create a positive impact of the society as a number of different citizen within that country benefit from this venture. Starting at the farmer who grows the crops to the companies which are involved in transporting and extracting the sugar from the cane to the companies who buy the sugar in its raw form to be sold to food companies. Everyone along the line benefits from this and also bring stability within the country. Another thing which it has an impact on is the environment. This is because if there was an oil spillage and had an effect on the land, the effects of this spillage would not be long last since the ethanol is straightforwardly biodegradable.

Various Advantages and Disadvantages of Biofuels

Disadvantages of Bioethanol

On the other hand, ethanol does have a number of disadvantages which some of them are similar to the shortcomings of fossil fuels. This can be argued by saying that the carbon emissions are similar to that of fossil fuels but they add up at different points. For fossil fuels the carbon emission is at the highest when it is burnt, however, for ethanol fuel this is not the case. Things which need to be taken into consideration are the amount of emissions that are released when the land needed for the crop to grow on is changed, and also when the transportation of the sugar cane to the milling site. And also how much emissions go into the atmosphere from this site. Another point which is extensively argued is deforestation. The amazon rainforest has a very bio-diverse range of inhabitants and this natural habitat is continuously threatened since a large amount of farmable land is required to grow crops.

As of 20th April 2017 the population of Brazil stood at approximately 210,900.000 and the total land area is roughly 8,349,00 km2. This means the argument which would erupt in this situation since the population is growing progressively is the food vs fuel argument. This when feedstock’s which are used to produce the ethanol are used for biofuel production instead of food. Also using the land for biofuel purposes instead of food

History of sugar cane

Sugar cane is a crop that is widely farmed across the world and amounts to approximately 70% of the world’s sugar. It goes by the scientific name of Saccharum Officinarum and was first found in the 14th century and it came from New guinea. Major expansion first began when Arab people were invading Persia and found sugar cane being produced and it was there how they learnt how to make sugar. Sugar is a general term for a great number of carbohydrates which distinctively has a sweet taste. The key sugar, glucose, is present in all plants and it is a by-product of the photosynthesis.

Sugar Cane Production process

In the sugar cane production process there are many steps involved which are all vital in its development.  Since sugar cane is a sub-tropical food crop it requires a lot of sunlight and water for its growth. However, this shouldn’t mean that the roots be exposed to too much water. The plant needs enough air to breathe so when the roots are too wet it limits the amount of air pockets. This causes the roots to be prone to diseases which is detrimental to its growth. It usually takes around about 9-12 months for the plant to grow in Brazil but this time varies across different places in the world.

The second step in sugar cane production is the harvesting of the plant. This can be done in a number of ways but it depends on the region or how big the field is where the sugar cane is growing. The main method of harvesting which is done globally is by using self-propelled harvesting machines. Either the farmer would already own the vehicle or they would hire machine owners to harvest their fields for them. Harvesting can be done manually by hand or by machine. Doing the job manually requires lot of manpower and it is an unclean job. When doing this job by hand the sugar cane would have to be cut from the stems and leave the roots alone. This ensures that the roots redevelop in time for the following yield. Another way which Is a hazard to the environment is burning the dead foliage which is around the sugar cane and also burn the waxy coating. The fire would scorch at a really high temperature but it would be done quickly so it doesn’t affect the sugar content or harm the stalk. The reason it is a hazard to the environment is because of the carbon content which is released when it burns. However local farmers argue that there little to nothing environmental impact since the C0which is released is nothing compared the C0needed for photosynthesis to occur in the first place.

After the harvesting stage is complete comes the extraction method. This involves several crucial aspects which flows into many different streams such as the efficiency of the extraction. This in the long run shows the profitability of the entire operation. After the sugar cane is harvested the sugar needs to be extracted immediately so that sugar is not lost. The sugar cane is crushed as soon as possible and thus with this crushing process creates syrup. The sugar cane is drowned in warm water in conveyor belts while strong water jets forcefully remove any debris from rocks or mud. This is put through many heavy duty rollers which are designed to squash the remaining pieces of sugar cane and thus extract the juice from its pulp.

Subsequently when this process is finished, its once again placed in water but this time the water is boiling.  In addition, lime is thrown into the mixture which aids in purification of the sugars. This mixture of adding heat and lime to the process helps remove any small particles to improve filterability. The sugar purification stage is also aided to form sugar crystals since the physical chemistry of the product helps it to only attach itself to other sugar crystals whilst rejecting the non-sugar and therefore ends up forming pure sucrose. The boiling step which occurs at this stage to signal the start of the process which is responsible for removing any small particles from the mixture. Once that stage is complete comes the evaporation stage. There a 3 vacuum pans in which the pulp of the sugar cane passes through. Initially it enters through vacuum A (V1) and this is where the boiling takes place. It is then put through vacuum B (V2) where at this stage, the crystal sugars are separated from the raw thick liquid which is known as Massecuite. Finally, it comes into vacuum C (V3) where it comes out as molasses.

The ethanol which is produced form different feedstock’s such as corn or the sugar beet which is grown in Europe are alike. They still have the same chemical properties and its virtually the same type of fuel which is produced. However, sugar cane has many advantages over its fellow feedstock’s such as being the one with highest yield per hectare of land for production. For this this size sugar cane is able to produced 7500 litres of ethanol which is quite high in comparison to its fellow counterparts. In comparison, corn in the United States would only produce 3800 litres of ethanol per hectare which is just above half of the quantity ethanol from sugar cane is produced at. As for sugar beet which is predominately produced in Europe due to having a colder climate than its counterpart has a yield of roughly 5500 litres of ethanol per hectare of land.

A really efficient by product of sugar cane is called bagasse. This waste product the is the fibre which remains after the extraction of sugar cane happens. Traditionally, bagasse was burnt was burnt in fields since it’s use was fairly limited and the emissions which came from burning aiding pollution, However, now there are many uses for this once thought waste product such as burning it in factories for steam production or for producing electricity. By burning it in the mills, the factories become self-sufficient and therefore are able to control their own energy input. Another thing which helps the creation of this electricity, is that the harvest season of the sugar cane over laps the driest period of the year, and so this aids the countries hydro-electricity as it isn’t able to produce as much as electricity.

Another use for bagasse is feeding it to cattle as an emergency feed. Bagasse can also be moulded into products which can be used for food services such as bowl and plates. The strength of these products are generally 50% stronger than their usual as well as have more durability and flexibility due to the starch component involved in the production process. One environmental benefit of bagasse is that it limits the amount of trees cut down to produce paper.


Sugar cane is a sub-tropical feedstock which requires plenty of sun and water for it to grow. Amongst this there are a further 3 requirements which aid in the growth and development of sugar cane. The 3 main requirements for utilising sugar from sugar cane are; air temperature, the amount of sun light and the final one being the right amount of rainfall to fall on the land. Sugar cane in its principle is a C4 plant and thus has the means to carry out higher photosynthesis rates which causes the stalk growth to be affected.

This graphs shows that the longer the hours of the sun shines on the crop, the quicker the sugar cane growth at. The second main requirement of sugarcane which aids in its growth and development is rainfall. The most preferable amount of rain required for its growth is in the region of 1100 and 1500 mm. This helps cause rapid cane growth as well as the lengthening of the plant. However, too much water can also be harmful to the plant especially in the course of ripening. The ripening of the plant is usually determined largely by the reducing sugars, the levels of sucrose of the plant and the stalk humidity during the harvest season. The sucrose in turn are usually at its highest during the last stages of sugarcane cycle which occurs during the end of December as the plant has low growth rate during this period.

The last factor for optimum growth is the temperature of air which has direct effect on the ripening of the plant as well as the growth of the sugarcane. The rate at which photosynthesis occurs changes when the temperature of air is reduced therefore a sugar which is made by photosynthesis is converted into sucrose.  During the last 3 months before the harvest period the air temperature plays a big role in the ripening since there is a drop in temperature. This decrease in the temperature during the last few months is more favourable for the conditions required for optimal ripening. Another thing this helps with is the amount of sugar produced in the plant.

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Sugar cane requires sub-tropical temperatures

Trade – off

To first establish a direct link between the trade-off between sugar and ethanol productions are number of things need to be taken into consideration. The first point to be considered is the utilisation of the land. Another important factor which needs to be considered is the amount of sugar cane and ethanol is produced. Brazil as a nation has the strongest economy in Latin America as well as being the world’s largest coffee producer. It also sits on 850 million hectares of land and is the fifth most populous country in the world in terms of size and inhabitants. Its coffee sector utilises more than 5 million individuals and contributes up to 40% of the entire world’s coffee supply.

Despite all of this, the problem with utilising the land needed to grow the feedstock which in this case is sugar cane, is that they use up the land which could have been used for food.  This could pose a problem for Brazil since poverty is widespread in some regions of the country and this could be down to the inequality of land tenure. In the early 1960s, a military dictatorship brought about a distinctive change to the country. Because of this millions of rural natives moved to the big cities for work due to the rapid industrialisations and mechanisations of agriculture. However, an Agrarian reform in 1990s enabled up to four million farms in Brazil to be given back to the citizen. Nevertheless, these farms were in size relatively small and this small scale type of farming was called family farming and it accounted for up 70% of the countries food production.

Another point which needs to be taken into consideration is the amount of sugar and ethanol is produced. During the harvest season of 2015-2016, the total amount of sugar cane produced was in the region of 666.824 (thousand tons) however this figure doesn’t represent the amount turned into sugar or into ethanol. The bulk of the amount of sugar cane produced came from the South-Central region of Brazil which produced 617,709 (thousand tons) which nearly 96% whilst the rest of the amount was contributed by the North-Northeast region of Brazil which produced 49,115 (thousand tons).  The total amount sugar cane produced was split into two categories; one was the sugar produced from it and the other factor was the amount of ethanol produced. The total sugar produced in Brazil from sugar cane came to 33,837(thousand tons) where once again this figure was majorly dominated by the South-Central region. The North-Northeast region only contributed a little over 7% at 2616(thousand tons).

The total amount of Ethanol produced was also split into two different kinds of Ethanol. The first type of Ethanol produced was Hydrous Ethanol at 17,581 (thousand m3). This type of ethanol is at its highest concentration without having to distil it even further. In addition, this fuel which is added to Ethanol and by doing so it lowers the operating temperatures of the engine because of the absorption of more water than its counterpart (Anhydrous). It also increases the octane level of the fuel and reduces the risk of knocking, in doing so, it further improves the thermodynamic efficiency of the engine whilst also improving fuel efficiency.  On the contrary, the amount of Anhydrous Ethanol produced is lower at a figure of 11,661(thousand m3). In this form, Ethanol is at highest level of purity since all traces of water are removed with its purity at 99.5%. Over 50% of Anhydrous Ethanol is produced in the Sao Paulo which is situated in the Southern region of Brazil.

Anhydrous Ethanol vs. Hydrous Ethanol in Gasoline Blending

The last and most key point which needs to be looked at before establishing a link between the trade-off of sugar and ethanol production is prices of sugar and Ethanol in Brazil.


Show trade-off in America

The United States of America is the biggest producer of Ethanol in the world. During the year 2015 it produced a record 30,983 (thousand tons) this was a rise of 2.9% increase from the previous year. Most of the biofuel produced in this country comes from corn which is the primarily the feedstock used.  Corn is the main feedstock which grown by farmers amounting for around 95% of the total feed grain which is produced. The amount of land in which corn is produced in the united states is approximately 90 million acres of land.

Figure: Corn yield and acreage

Over the years the graph shows that the amount of farmable lands being used for corn has been decreasing very slowly as well as some years increasing very slowly once again.  Although the amount of acres used to produce the corn roughly the same amount of land that was used 10 years ago (2006/2007), the amount of yield produced is greatly higher. This is due to the fact that in recent years there has been a strong demand for ethanol as fuel and therefore farmers have adapted to creating initiative methods to getting more corn out of lands which was not previously suitable for farming.  The way they have done this is by introducing different types of seed varieties to maximise growth of the crop and by also introducing a number of different fertilisers and pesticides to avoid the crops getting spoiled. The farmers have also decided to mainly focusing on corn as there is a huge demand and by doing so crops such as soybeans have seen a decline in their production.

As shown in the figure above, it can be seen as production rate increases so does the average weighted farm price. The price increase over the years could be seen as an incentive to farmers who grow the feedstock and make it more profitable for them. Also other factors which could have affected the trend is the availability of the feedstock. Another cause for the price increase could be that the if the demand for ethanol is increased so does the feedstock which required to produce the fuel.

From Table.., during the market year of 1989/90 the average Corn price was at 2.36(US Dollars) and this was for per bushel of Corn. In the next decade or so the prices for corn went either up or down, and in the market year of 1999/00 the price was at 1.82 us dollars per bushel corn which resulted in a decrease of almost 30%. The market year following that which was 2000/01 at 1.82 (us dollars) and it reached its highest recorded price during the market time period of 2012/13. The reason for this really high price which was almost an increase of 192% from the year 1989/90 was due to the fact there was a severe drought which hit parts of the USA and therefore due to limited crop demand increased. Also another effect which the drought had was that the amount of corn exported by the US decreased leading to an increase in price. So in 27 years from 1989 to 2016 the average price of corn saw an increase of approximately 53%.

Table: Corn Average prices in US (Per Bushel)

When corn was first planted in USA it was initially a produce which would provide as food source for human and animals. However, over the years when it was discovered to be a feedstock for producing Ethanol and the many industrial uses it has, the consumption in that sector has grown. The amount of corn used every year for feed and residual use has remained steady but there was a slight dip in the year 2012/13 as previously explained earlier that this was due to drought that occurred during that period which had an impact on the data. One thing which had a large increase in the past decade was the amount of corn which was produced and was used to produce alcohol for the purposes of using as a fuel. Also another thing which can be seen from the figure above is that corn which was produced for the food sector has been increasing. A reason for this could be the US’s adopted methods from Latin America which utilises corn for various foods and snack such as; tacos, enchiladas and tortillas.

Figure: US Corn Exports (1000 Bushels) database/feed-grains-yearbook-tables.aspx

As shown in the table above, the export rates always been in the region of 2 million (1000 bushels) until the harvest year of 2011/2012 where export rates dropped slightly. However, the biggest difference could be seen in the year 2012/2013 where the export rate decreases by over 50%. This dramatic loss was due to the climate as drought heavily affected the growth of the crops. Because of these reasons the prices of the corn increased which subsequently increased export and import prices and this is why the export amount is lower than the previous years. The graph also shows that once the harvest year ended and the new harvest year started the number of corn exported returned to its normal position.

Fuel Consumption

In figure 1 and 2, the difference in the consumption of the two charts is pretty clear. As shown above the Hydrous blend of Ethanol fuel is more used. Hydrous which mean more wet is more widely used since it is has strong concentration level that Anhydrous Ethanol. The reason for this is that since Hydrous Ethanol is more wet and so that the water particles have strong hydrogen bonds and when it comes into contact with the oxygen and hydrogen bonds within the Ethanol, the hydrogen molecules bridge over and therefore create strong molecular forces. Since the molecular forces have become strengthened it leads to the reduction of Nitrogen emissions which are emitted from the vehicle. A number of positive impacts are listed below:

  • Compression rate of the engine is increased due to ethanol
  • Overcomes knocking effect in the engine
  • More efficient than its Anhydrous counterpart
  • Octane level is increased and therefore efficiency of engine at its highest

This shows what corn is used for in the US and its purposes. Firstly, in the 3 years shown in the data there were steady increase in the amount of uses corn production had such as the conversion of corn for alcohol for fuel purposes. Another contribution corn had was the manufacturing of alcohol but this time for beverages, this also an increase over the years presented. One by-product of corn which is produced by the wet milling process to ultimately create ethanol is a product called High-fructose corn syrup or in short HFCS. This syrup in recent years has replaced sugar to become the main sweetener due to the fact that it is simply cheaper than sugar.

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[4]  25/12/16

[5]  25/12/16



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