Enzyme and Microbial Technology 33 (2003) 71–78The effect of Tween-20 on simultaneous saccharification andMalek Alkasrawi , Torny Eriksson , Johan Börjesson , Anders Wingren ,Mats Galbe , Folke Tjerneld , Guido Zacchi a Department of Chemical Engineering 1, Lund University P.O. Box 124, SE-221 00 Lund, Sweden b Department of Biochemistry, Lund University P.O. Box 124, SE-221 00 Lun
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Microsoft word - 026_roccuzzoEffects of Organic Fertilisation on ‘Valencia late’ Orange Bearing Trees
B. Torrisi, P. Rapisarda and F. Intrigliolo CRA - Research Centre for the Soil Plant System (CRA-RPS)
Keywords: δ15N, leaf analysis, fruit quality, canonical discriminant analysis
In a study realised over a three year period on orange bearing trees (Citrus
sinensis (L.) Osbeck) ‘Valencia late’, grafted on sour orange (C. aurantium L.), the
effect of organic fertilisers (OF) on plant nutrition and performance was verified. In a
randomized block experimental design, four treatments were compared, namely:
mineral fertiliser (MF) treatment adopted as control, citrus byproduct compost (CB),
poultry manure (PM) and livestock waste compost (LW). The trees, with the exception
of (MF) treatment, were organically grown since 1994 in the experimental farm of
CRA-ACM in Lentini, Sicily, and received the same N input every year.
Significant differences for micronutrients (Fe, Mn, Zn) were noticed in leaf
analyses, whereas no difference was found between treatments for leaf macronutrient
content. The δ15N detected in leaves, proteins of pulp and amino acids of juice showed
the lower level in MF, an intermediate value in CB and the highest level in animal
derived fertilisers treatments (PM and LW). Fruit of the CB treatment showed values
of total soluble solids and total acidity significantly lower than other treatments.
Orange peel Chroma C* in CB and MF was higher than in PM and LW treatments.
Discriminant analysis of the leaf and fruit analytical data set successfully
separated treatments. First discriminant canonical function explains the 96,9% of the
variability, with highly significant Wilks’ lambda. Cross validation classified correctly
all MF and CB samples, whereas PM and LW in few cases were mixed up.
Despite the increase of importance of organic citrus industry, the behavior of organic fertilisers (OF) and their ability either to satisfy orange trees nutrients requirements and to optimize soil properties are not yet well known, especially for orange bearing trees which have high nutrients demand (Canali, 2003). The large increase of organic citrus industry in Italy and the shortage of data on long term application of organic fertilisers (OF) in citrus groves soils imply the necessity to identify agro-ecologic markers for either management control and fruit traceability. The aim of the study was to: i) verify OF effects on orange trees nutritional status, yield and fruit quality and ii) select plant and fruit parameters useful for organic citrus MATERIALS AND METHODS
The study was realised between 2003 and 2006 in CRA-ACM experimental farm “Palazzelli”, eastern Sicily (37°17’56”76N; 14°50’29”76E), in a 2 ha “Valencia late” sweet orange orchard (Citrus sinensis (L) Osbeck), grafted on sour orange (C. aurantium L.), planted in a 6×4 m design on a sandy loam soil. Proc. XXVIIIth IHC – IS on Organic Horticulture: Four fertiliser treatments were applied: citrus by-products compost (CB), poultry manure (PM), livestock waste compost (LW) and mineral fertiliser (MF), as control. The trees, with the exception of MF treatment, were organically grown since 1994 and received annually the same N input. Treatments were distributed in three blocks of 4 plots of 60 plants each; in each block 8 index plants were selected for all soil and plant material Plant nutritional status was determined by foliar analysis performed on 80 spring- cycle leaves of the index trees collected in each plot in October from terminal, non- fruiting shoots (Embleton et al., 1973). Total yield was recorded in field for each index plant. Mean weight and fruit physical and chemical parameters were determined in a sample of 40 fruits collected at harvest from the outer part of the canopy of the index trees. The leaves were: i) washed in tap water by rubbing both sides using cheesecloth, ii) rinsed in deionised water, iii) oven dried at 65°C for 72 h, iv) ground and v) dried at 105°C for 4 h. The concentration of N was determined on 1 g of ground leaf tissue using the micro-Kjeldahl method (Büchi Distillation Unit K370). Another 1 g of ground leaf tissue was ashed in a muffle furnace at 550°C for 12 h. After incineration and extraction with nitric acid (1% v/v), P, K, Ca, Mg, Fe, Zn and Mn were determined using inductive coupled plasma-optical emission spectrometry (ICP-OES; OPTIMA 2000DV, Perkin- On each fruit sample, physical parameters (firmness, fruit weight, width of the central axis, and peel thickness) were measured using standard methods (Wardowski et al., 1979). Fruit color measurements were realized with a portable spectrophotometer (CM-2550d, Konica Minolta Italia). Each sample of fruits was squeezed, and juice content, total acidity (TA) and total soluble solids (TSS) were determined. Vitamin C was analyzed by high-performance liquid chromatography (HPLC) (Rapisarda and Intelisano, 1996). Synephrine content was determined by the HPLC method described by Rapisarda Measurement of the 15N/14N ratio of leaves, pulp and amino acids of juice were realized following the methods described by Bricout and Koziet (1987) with slight modification. For the measurement, an isotope ratio mass spectrometer (Delta plus XP ThermoFinnigan, Bremen, Germany) equipped with an elemental analyzer (EA Flash 1112 ThermoFinnigan) was used. The values were expressed in δ‰ against international standards (air for δ15N). The isotopic values were calculated against working in-house standards (mainly casein), calibrated against L-glutamic acid USGS 40. The uncertainty ANOVA was performed and mean values separated with Tukey HSD test (SPSS package ver. 18). Moreover, data were processed by means of canonical discriminant analysis (CDA) to evaluate all parameters at the same time and detect those that mostly
RESULTS AND DISCUSSION
No significant difference between treatments was noticed for leaf N, K and P contents, whereas significant differences for Ca, Mg and micronutrients were observed in leaf analyses (Table 1). Ca content was higher in CB only in respect to LW; the latter also showed Mg values lower than other treatments. All values for macronutrients were in the optimal range according to the international standard for diagnosing nutritional status CB leaves constantly showed higher micronutrient content, in the case of iron compared to PM and LW, for manganese compared to PM and for zinc compared to MF. Even though leaf levels of Mn and Zn were deficient, no deficiency symptoms were No significant difference was noticed for yield (Table 2). Regarding fruit quality parameters, CB treatment showed values of total soluble solids and total acidity lower than other treatments; this result had no relevance on maturity index (TSS/TA). Total N content in juice was higher in LW and MF compared to PM and CB treatments. Orange peel Chroma C* in CB and MF was higher than in LW treatment; PM showed the lowest value. No difference among treatments was recorded for fruit weight, firmness, peel thickness, central axis, juice content, vitamin C and synephrine (data not shown). The δ15N detected in leaves (Table 1) and amino acids of juice (Table 2) showed the lowest level in MF, an intermediate value in CB and the higher level in animal derived fertilisers (PM and LW). In the case of δ15N in proteins of pulp the complete separation Discriminant analysis of overall leaf and fruit analytical data set successfully separated treatments. First discriminant canonical function explains 96.9% of the variability with highly significant Wilks’ lambda (Table 3). Values indicate the weight of each variable on separation between groups for each discriminant function. Pulp δ15N, leaf N and K levels and juice acidity showed higher relative weights. Standardized discriminant canonical scores of function 1 and 2 are plotted in Figure 1. Distribution of points allows visualizing clearly the separation of groups, and the predominant effect of function 1. PM and LW treatments were not clearly differentiated. As a matter of fact cross validation classified correctly all MF and CB samples (100% of cases) whereas PM and LW were mixed up in few cases. CONCLUSIONS
Organic fertilisers showed to assure in the medium period a balanced nutritional status of citrus trees, comparable to mineral fertilisers. The compost from citrus by- products (CB) seemed to have higher nutrient use efficiency, probably due to the effect of higher organic matter addition and the related effects on soil biological fertility (Srivastava et al., 2002; Toselli, 2010). OF treatments showed yield levels similar to MF, thus demonstrating that fertiliser treatments did not affect productivity, whereas some fruit quality parameters were The fertilization regimens may be sufficient to produce differences between treatments if levels of N-containing compounds vary, but in our experiment no differences were observed in synephrine content between organic and conventional fruits with equal level of total N applied to the different plots. 15N tracing in plots fertilised with animal by- products (PM and LW) showed some differences with plant derived fertiliser (CB), but main differences were noticed in the comparison all OF with synthetic mineral fertiliser. Multivariate approach by means of discriminant analysis succeeded to highlight the effects of fertiliser treatments namely, mineral, plant based organic and animal based organic fertilisation. δ15N was confirmed to be a good indicator for management discrimination (Rapisarda et al., 2005, 2010), but few other leaf (N, K) and fruit parameters (acidity) affected the separation between data sets, too. ACKNOWLEDGEMENTS
This study was realized in the project “Advanced Researches in Citriculture and their applications” (RAVAGRU) funded by the Italian Ministry of Agriculture, Food and Forestry Policies (MiPAAF) - Pub. No. 41.
Bricout, J. and Koziet, J. 1987. Control of authenticity of orange juice by isotopic analysis. J. Agric. Food Chem. 35:758-760. Canali, S. 2003. Soil quality of organically managed citrus orchards in the Mediterranean area. p.115 125. In Organic Agriculture: Sustainability, Markets and Policies. OECD, Embleton, T.W., Jones, W.W., Labanauskas, C.K. and Reuther, W. 1973. Leaf analysis as a diagnostic tool and guide for fertilization. p.183-210. In: W. Reuther (ed.), The Citrus Industry, Vol. III, Production Technology, University of California, CA. Rapisarda, P. and Intelisano, S. 1996. Sample preparation for vitamin C analysis of pigmented orange juices. Ital. J. Food Sci. 3:251-256. Rapisarda, P., Calabretta, M.L., Romano, G. and Intrigliolo, F. 2005. Nitrogen metabolism components as a tool to discriminate between organic and conventional citrus fruits. J. Rapisarda, P., Camin, F., Faedi, W., Paoletti, F. and Tobileo, M.R. 2010. New markers for the traceability of organic fruit. Acta Hort. 873:173-183. Srivastava, A.K., Singh, S. and Marathe, R.H. 2002. Organic citrus: soil fertility and plant Toselli, M. 2010. Nutritional implications of organic management in fruit tree production. Wardowski, W., Soule, J., Grierson, W. and Westbrook, G. 1979. Minimum Quality (Maturity) Standards. In: Florida Citrus Quality Tests; Florida Cooperative Extension Service, IFAS, University of Florida: FL. Table 1. Leaf analysis results on dry matter basis (mean of 3 years). 1 Mean separation at 5% level with Tukey HSD test. Table 2. Values of yield and main fruit parameters (mean of 3 years). 1 Mean separation at 5% level with Tukey HSD test. Table 3. Standardized canonical discriminant function coefficients.
Fig. 1. Canonical discriminant functions 1 vs. 2.
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