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Seed quality diversity, trait associations and grouping of accessions in Jatropha curcas L.
Industrial Crops and Products 51 (2013) 178–185
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Seed quality diversity, trait associations and grouping of accessions in Jatropha curcas L. Juan M. Montes a,∗ , Frank Technow b , Brigitte Bohlinger c,d , Klaus Becker a,c,d a
JatroSelect GmbH, Echterdinger Str. 30, 70599 Stuttgart, Germany Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70599 Stuttgart, Germany Institute of Animal Production in the Tropics and Subtropics, University of Hohenheim, Fruwirthstr. 12, 70593 Stuttgart, Germany d JatroSolutions GmbH, Echterdinger Str. 30, 70599 Stuttgart, Germany b c
a r t i c l e
i n f o
Article history: Received 28 May 2013 Received in revised form 13 August 2013 Accepted 15 August 2013 Keywords: Jatropha Seed quality Kernel Shell Germplasm Breeding
a b s t r a c t Jatropha curcas L. (jatropha) has recently received great attention for its utilization in biofuel production, rehabilitation of wasteland and rural development. Improvement of seed quality is an important breeding goal but jatropha seed quality has been investigated with sample sets comprising a small number of traits, accessions and environments. Our main goal was to investigate a large number of traits in a wide and geographical diverse collection of seed samples to have a comprehensive view on the phenotypic variation of seed quality in jatropha. Our objectives were to (i) assess phenotypic variation of jatropha seed quality traits, (ii) investigate the association among those traits (iii) group germplasm and (iv) examine the partition of the trait variation attributed to factors of geographical origin (world regions and countries) and sampling procedures (single seeds vs. seed samples). Phenotypic variation was larger than reported previously. We detected a strong positive association between seed weight and the contents of oil in seed and kernel. Oil content in seed was negatively associated with the ratio of shell weight to seed weight. Contents of oil and protein in kernel were also associated negatively. Accessions from Africa and Central and North America grouped together and separated of accessions from Asia and South America. Countries and accessions within countries contributed most to the total variance of seed quality traits. Determination of seed quality among accessions using seed samples is more efficient than using single seeds. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Jatropha curcas L. (jatropha) is an important undomesticated plant which has received great attention in recent years for its utilization in biodiesel production (Ceasar and Ignacimuthu, 2011). JCL is a small tree or large shrub, which belongs to the Euphorbiaceae family and has a life expectancy of up to 50 years (Divakara et al., 2010). The plant has a high potential for greening and rehabilitation of wastelands and the seeds have a high oil concentration with excellent quality for conversion into biodiesel (Francis et al., 2005). Although various efforts have been made to develop jatropha as an industrial crop (Fairless, 2007; Sanderson, 2009), the absence of improved cultivars and lack of agronomic knowledge represent the main bottleneck that limits the full exploitation of this plant’s potential (King et al., 2009). Improvement of seed quality is an important breeding goal but seed quality diversity in jatropha has been investigated with
∗ Corresponding author. Tel.: +49 0711 459 99771; fax: +49 0711 459 99789. E-mail address: [email protected] (J.M. Montes). 0926-6690/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.indcrop.2013.08.046
datasets comprising a small number of traits, accessions and environments (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). A more comprehensive assessment of phenotypic variation of seed quality can be performed by analysis of larger datasets including additional quality traits and a larger number of accessions collected in different climatic regions where jatropha grows predominately. Plant breeders usually exploit the associations among traits to optimize breeding programs by means of indirect selection procedures (Bernardo, 2002). In jatropha, a positive linear association between oil content and seed weight was reported (Rao et al., 2008) and associations among contents of fatty acids were investigated (Ovando-Medina et al., 2011). Expanding the matrix of traits to important quality traits such as contents of protein and phorbol ester (the chemical compound causing toxicity), might reveal new trait associations useful to increase efficiency in breeding programs. Knowledge of common characteristics of individuals can assist breeders to identify germplasm sources which have potential utility for specific breeding objectives (Rincon et al., 1996). Besides, cluster analysis based on phenotypic data might assist to identify
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185 Table 1 Geographical origin and sampling structure of the 370 jatropha seed samples utilized in this study. Sample code
N
Country
World region
JCL001-038 JCL039-043 JCL044-066 JCL067-069 JCL070 JCL071-076 JCL077-084 JCL085-086 JCL087-093 JCL094-095 JCL096-125 JCL126 JCL127-139 JCL140-291 JCL292-330 JCL331 JCL332-368 JCL369 JCL370
38 5 23 3 1 6 8 2 7 2 30 1 13 152b 39 1 37 1 1
Argentina Bangladesh Brazil Cameroon Cape Verde Chad Colombiaa Costa Rica Egypt Gambia India Indonesia Laos Madagascar Mexico Nicaragua Paraguay Sri Lanka Tanzania
SAM ASIA SAM AFRICA AFRICA AFRICA CNAM CNAM AFRICA AFRICA ASIA ASIA ASIA AFRICA CNAM CNAM SAM ASIA AFRICA
a
Seeds originated from Salvador planted in Colombia. 102 samples are from 34 accessions sampled in triplicate. The rest of the samples corresponded to single accessions. SAM = South America; CNAM = Central and North America.
179
only. The traits measured on single seed basis were seed weight, kernel weight, shell weight, seed length, seed width and seed thickness. The fatty acids profile was measured in the second batch only: myristic acid (C14:0), palmitic acid (C16:0), sapienic acid (C16:1), margaric acid (C17:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), arachidic acid (C20:0). The following traits were measured on both batches: oil content in seeds, seed weight, oil content in kernel, protein content in kernel, content of phorbol ester in kernel, ash content in kernel, acid detergent fiber content in shells, acid detergent lignin content in shells and ash content in shells. 2.2. Measurements of single seeds A total of 20 seeds per sample were individually measured. The length, width and thickness (distance from standing point to highest point of a free standing seed on a table) and seed weight were measured firstly. The shell and kernel of each seed were then separated and their weights were measured. The proportion of shell in seed was calculated as the ratio of shell weight to seed weight. The total number of single seed measurements was 3000.
b
geographical regions where genetic and/or environmental factors are favoring seed quality. A more comprehensive assessment of seed quality diversity in jatropha need to be performed with datasets comprising a large number of quality traits in a large number of geographical diverse accessions. Despite the various studies on phenotypic variation carried out, the conclusions from them are limited to the materials investigated which in most cases are of local relevance (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). Our main goal was to establish a wide collection of seed samples with a significant number of accessions from different origins (Africa, Asia and America) to have a comprehensive view on the phenotypic variation in jatropha. To our knowledge, the germplasm collection analyzed in this study comprises the largest collection (302 accessions) reported till present. Our objectives were to (i) assess the phenotypic variation of jatropha seed quality traits, (ii) investigate the association among those traits, (iii) group germplasm to identify accessions which have potential utility for specific breeding purposes and (iv) examine the partition of the trait variation attributed to factors of geographical origin (world regions and countries) and sampling procedures (single seeds vs. seed samples). 2. Materials and methods 2.1. Plant material and seed traits Seed samples were taken from JatroSelect GmbH germplasm bank. The seeds were collected from single trees during collection travels in the time period between 2008 and 2010. The amount of seed available for each accession in the bank ranged between 0.2 and 0.5 kg. A total of 302 accessions were selected from the germplasm bank and seed samples of 40 seeds were taken from the stored seeds of each accession. To assess the variation among seed samples harvested within a single tree, 34 accessions were sampled in triplicate. The final sample set comprised a total of 370 seed samples that originated from a wide range of geographical regions worldwide (Table 1). The samples were processed in two batches. The first batch comprised 150 samples and the second batch comprised 220 samples. Single seed measurements were performed in the first batch
2.3. Oil content and seed weight in seeds The content of oil in seeds was determined with seed analyzer minispec mq7.5 (Bruker Optik GmbH, Rheinstetten, Germany) based on nuclear magnetic resonance (NMR). Seed weight was calculated as the weight of the seed sample divided by 40 (the number of seeds in the sample). 2.4. Wet chemistry analysis of kernel and shell fractions The seed samples were de-shelled and the resulting kernel and shell fractions were analyzed separately. Kernels and shells were ground using a IKA-A11 basic grinder to 1.5 mm (kernel) and 1 mm (shell) particle size. The ground materials were subsequently used for wet chemistry analysis. For the kernel fraction, the oil content was determined with petroleum ether in a Tecator Soxhlet type extractor for 6 h until the evaporation of residual ether revealed that it contained <0.2% lipid. The oil concentration of the samples was determined gravimetrically (AOAC, 1990). The nitrogen content was determined using a C/N-Macro-Analyzer (C/N vario MAX, Elementar Analysensysteme, Hanau, Germany) according to the Dumas principle. The protein content was calculated as nitrogen content × 6.25 (ISO 16634-1: 2008). Concentration of phorbol ester was determined in at least two duplicates of each sample (Devappa et al., 2010). Separation and identification of fatty acid methyl esters (FAMEs) were carried out in at least two duplicates of each sample by the boron trifluoride method (AOAC, 1990). Content of ash in kernel was determined by difference in weight before and after incineration in a muffle furnace at 500 ◦ C for 6 h (AOAC, 1990). For the shell fraction, acid detergent fiber and acid detergent lignin were determined (Van Soest et al., 1991). Content of ash in shells was determined by following the same procedure as for the determination of content of ash in kernel. All values were expressed on dry matter basis. 2.5. Statistical analysis All computations were performed within the R statistical environment (R Core Team, 2012). Descriptive statistics (mean, minimum, maximum and phenotypic coefficient of variation) were calculated for all traits analyzed. Trait associations were assessed by Spearman rank correlation to avoid scale-dependent correlations. The grouping structure of the material was investigated with
yijk = ˇ0 + Ri + C/Rij + G/C/Rijk
(1)
where yijk is a phenotypic observation made on the kth accession within the jth country of the ith world region, ˇ0 is a common intercept, Ri is the effect of the ith world region, C/Rij the effect of the jth country within the ith world region and G/C/Rij the effect of the kth accession from the jth country in world region i, which is confounded with the residual. All effects but ˇ0 were taken as random. The following linear mixed model was used to investigate the variance components for accessions, seed samples and single seeds with data of the 34 accessions sampled in triplicate: yijk = ˇ0 + Gi + S/Gij + K/S/Gijk
(2)
where yijk is a phenotypic observation made on the kth seed within the jth seed sample of the ith accession, ˇ0 is a common intercept, Gi is the effect of ith accession, S/Gij the effect of the jth seed sample of the ith accesion and K/S/Gijk the effect of the kth seed within the jth seed sample of the ith accession, again, confounded with the residual. All effects but ˇ0 were taken as random. For the traits where no single seed measurement were available, the model was reduced as below, yijk = ˇ0 + Gi + S/Gij
SW1 KW
ASHS
ADL
ADF
ASHK
CP
PE
OIL
CL
SSP
S W2
ST
SL
S WD
a principal component analysis (PCA), using trait data in common to all 302 accessions. The PCA was conducted across world regions and separately for each. All variables were scaled to unit variance and centered to mean zero. We used R function “princomp” for computing the principal components. We investigated the variance components for world regions, countries and accessions by using data of the first and second batches jointly and the following model:
KW
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
SW
180
1
0.8
SW 0.6
SL S WD
0.4
ST 0.2
SSP SW2
0
OIL −0.2
CL CP
−0.4
PE −0.6
ASHK ADF
−0.8
ADL 1
Fig. 1. Correlation matrix of traits measured in 150 seed samples of jatropha. Traits measured on single seeds: SW1: seed weight; KW: kernel weight; SW: shell weight; SL: seed length; SWD: seed width; ST: seed thickness; SSP: proportion of shell in seed. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell (%).
(3)
where S/Gij now also contains variation between single seeds as well as residual variation. We used the R mixed model package “lme4” (Bates et al., 2012) for fitting the models with REML. For straightforward interpretation, the variance components were expressed as percentage of the total variance. 3. Results The sample collection comprised a large number of accessions (302) from four world regions and 19 countries where jatropha grows predominantly (Table 1). The collection comprised 104 accessions from Africa, 50 from Asia, 50 from Central and North America and 98 from South America. This resulted in a geographical contribution for the world regions of 34% for Africa (AFRICA), 17% for Asia (ASIA), 17% for Central and North America (CNAM) and 32% for South America (SAM). Seed quality variation measured on single seeds was high for seed weight, kernel weight, shell weight and the ratio of shell weight to seed weight and low for seed length, seed width and seed thickness (Table 2). For the traits measured on seed samples, the variation was high for seed weight, oil content in seed, phorbol ester content, ash content in kernel, C16:1, C18:0, C18:3, C20:0 and ash content in shell; intermediate for oil content in kernel, protein content in kernel, C18:1 and C18:2; and low for C16:0, acid detergent fiber content and acid detergent lignin content. The traits C14:0 and C17:0 showed an extremely high variation due to the presence of highly contrasting values in the sample sets. The correlation matrix of traits in the first batch of samples revealed a high positive association of seed weight in single seeds with kernel weight, shell weight, seed length, seed thickness and seed weight in seed samples (Fig. 1). A high and negative association was found between the ratio of shell weight to seed weight
and oil content in seed. Oil content in seed and oil content in kernel were highly positively associated and the protein content was highly negative associated with oil content in seed and oil content in kernel. The ash content in kernel was highly negatively associated with the oil content in seed and oil content in kernel. The traits acid detergent fiber and acid detergent lignin showed a high positive association. The correlation matrix of traits in the second batch of samples confirmed the positive association of seed weight with oil content in seed and oil content in kernel as well as the association between oil content in seed and oil content in kernel (Fig. 2). The negative association between protein content with oil content in kernel and oil content in seed was also confirmed. A high negative association was observed between seed weight and ash content in kernel, which was not observed for the first batch of samples. High and negative associations were observed between C18:3 and seed weight, oil content in seed and oil content in kernel. The traits C18:1 and C18:2 showed a high and negative association. There was a positive association between acid detergent fiber and acid detergent lignin. The combined analysis of trait associations with both batches of samples showed a high positive association of seed weight with oil content in seed and oil content in kernel and between oil content in seed and oil content in kernel (Fig. 3). A high negative association between oil content in kernel and protein content was observed and to a lesser extent of the latter with oil content in seed. The ash content in kernel showed a high negative association with oil content in seed, oil content in kernel and seed weight. Other trait associations were weaker. The PCA of the world regions resulted in two general groups (Fig. 4). One group comprised predominantly accessions from AFRICA and CNAM, the other group accessions from ASIA and SAM. The main traits affecting the first principal component were oil content in seed and oil content in kernel. The analysis
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
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Table 2 Descriptive statistics of traits measured on single seeds and seed samples of jatropha. Material
Trait
Mean
Min
Max
Single seeds
Seed weight (g) Kernel weight (g) Shell weight (g) Seed length (cm) Seed width (cm) Seed thickness (cm) Shell to seed ratio
0.6 0.4 0.2 1.7 1.1 0.8 36.4
0.1 0.0 0.1 1.1 0.8 0.6 24.6
0.9 0.6 0.4 2.3 1.6 1.1 100.0
CV 18.6 22.4 16.6 7.0 5.4 7.0 19.9
Seed samples
Seed weight (g) Oil (%)
0.6 31.7
0.3 10.6
0.8 41.0
17.9 19.1
Kernel
Oil (%) Protein (%) Phorbol ester (mg g−1 ) Ash (%) C14:0 (%) C16:0 (%) C16:1 (%) C17:0 (%) C18:0 (%) C18:1 (%) C18:2 (%) C18:3 (%) C20:0 (%)
53.5 27.5 4.6 4.9 0.0 14.0 0.9 0.0 5.3 40.3 39.0 0.2 0.2
28.5 18.4 0.0 2.5 0.0 10.8 0.4 0.0 3.5 29.0 20.0 0.1 0.0
65.6 37.7 10.3 9.0 0.3 22.0 2.3 1.9 17.7 54.4 50.0 0.5 0.3
12.7 13.1 55.6 22.4 194.3 8.2 24.5 503.7 29.6 10.9 13.8 29.9 19.1
Shell
Acid detergent fiber (%) Acid detergent lignin (%) Ash (%)
76.5 41.0 4.4
69.2 20.7 2.2
87.2 48.3 8.7
3.3 5.5 22.4
C14:0: myristic acid; C16:0: palmitic acid; C16:1: sapienic acid; C17:0: margaric acid; C18:0: stearic acid; C18:1: oleic acid; C18:2: linoleic acid; C18:3: linolenic acid and C20:0: arachidic acid.
within AFRICA resulted in one major group of accessions from Madagascar grouped together and a few accessions from Egypt, Cameroon and Chad that grouped separately. The analysis within ASIA showed three groups formed with accessions from Laos, India and Bangladesh. The analysis of SAM revealed a larger distance between accessions from Brazil and Paraguay with the accession from Argentina forming a bridge between them. The analysis of CNAM revealed that accession from Mexico grouped together with those from Costa Rica and accessions from Colombia grouping together with an accession from Nicaragua. In all cases, the main traits affecting the first principal component were oil content in seed and oil content in kernel (Table S1). Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.indcrop.2013.08.046. The variance component analysis showed that the larger fraction of the variation is present among countries within a world region and accessions within a country (Table 3). The variance component analysis of the traits analyzed on single seeds revealed a high seed to seed variation (Table 4). The traits analyzed in seed samples resulted in a larger fraction of the variation attributed
to accessions in comparison to the single seed measurements (Fig. 5). 4. Discussion Characterization and grouping of germplasm, knowledge of trait associations and appropriate sampling procedures are important aspects to be considered in breeding programs. Most of the studies performed till present comprised a reduced number of quality traits examined in a low number of accessions or the accessions were collected in a narrow geographical area (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). This situation limits a comprehensive assessment of the seed quality diversity in jatropha. The seed quality variation observed in our panel of 302 accessions was larger than the variation observed in previous studies (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013) for all the traits investigated. The larger phenotypic variation in our study might be explained by the inclusion of a larger and more diverse panel of jatropha accessions comprising larger genetic and environmental effects.
Table 3 Percentage of the total variance attributed to the variation among world regions, countries within world regions and accessions within countries and world regions (which is confounded with the residuals) for seed quality traits of 302 jatropha accessions. Variation Among world regions Among countries within world regions Among accessions within countries and world regions
Df
Seed weight
Oil content in seed
Oil content in kernel
Protein content in kernel
Phorbol ester in kernel
Ash content in kernel
Acid detergent fiber in shell
Acid detergent lignin in shell
Ash content in shell
3
0.0
0.0
0.0
0.0
14.6
18.8
0.4
2.5
7.6
15
70.6
70.0
69.3
50.5
34.7
44.2
42.4
29.8
9.6
283
29.4
30.0
30.7
49.5
50.7
37.0
57.2
67.7
82.8
Df: degree of freedom (approximate values returned by R function “aov”).
182
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Table 4 Percentage of the total variance attributed to the variation among accessions, seed samples within accessions and single seeds within seed samples and accessions (which is confounded with the residual) for seed quality of 34 jatropha accessions sampled in triplicate. Variation
Df
SW1
KW
SW
SL
ST
SWD
SSP
SW2
OIL
CL
CP
PE
ASHK
ADF
ADL
ASHS
Accessions Seed samples Single seeds
33 68 1938
32.8 0.0 67.2
21.9 0.0 78.1
55.7 0.0 44.3
34.8 0.3 65.0
30.8 0.0 69.2
36.1 0.0 63.9
5.1 0.0 94.9
93.1 6.9
91.6 8.5
92.6 7.4
95.3 4.7
93.3 6.7
89.1 10.9
61.6 38.4
47.7 52.3
70.0 30.0
ASHS
ADL
ADF
ASHK
PE
CP
CL
The quantification of the trait variation attributed to genetic and environmental factors cannot be performed with the data available in our study. Those studies need to be performed with seeds harvested from field experiments that have been managed similarly in terms of harvest and post-harvest procedures. Such studies will reveal the genetic and environmental basis of important quality traits. The analysis of trait associations in the first batch of samples showed a positive association among seed weight measured on single seed basis, seed weight measured on seed samples, kernel weight, shell weight, seed length, seed width and seed thickness. It is expected that those traits are related by dimensions of form and weight (Fig. 1). The high association between seed weight measured on single seed basis and seed weight measured in seed samples indicates that the single seed weight can be measured on group of seeds more efficiently than measuring single seeds. Similarly, the high association between oil content in seed and oil content in kernel indicated that measures collected on the group of seeds with the NMR equipment is the best method to determine oil content in seeds and indirectly in kernels. By using NMR methodology, seed weight, oil content in seed and moisture content can be measured very efficiently in one step.
OIL
Df: degree of freedom; traits measured on single seeds basis: SW1: seed weight; KW: kernel weight; SW: shell weight; SL: seed length; SWD: seed width; ST: seed thickness; SSP: proportion of shell in seed; traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
1
S W2 0.8
OIL
0.6
0.4
CL
0.2
CP
0
PE −0.2
ASHK
−0.4
−0.6
ADF
ADL
−0.8
S W2 OIL
ASHS
ADF
ADL
C20_0
C18_3
C18_1
C18_2
C17_0
C18_0
C16_1
C14_0
C16_0
ASHK
PE
CL
CP
OIL
−1
1
Fig. 3. Correlation matrix of traits measured in 370 seed samples of jatropha. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
0.8
CL CP
0.6
PE 0.4
ASHK C14_0
0.2
C16_0 C16_1
0
C17_0 −0.2
C18_0 C18_1
−0.4
C18_2 C18_3
−0.6
C20_0 −0.8
ADF ADL
−1
Fig. 2. Correlation matrix of traits measured in 220 seed samples of jatropha. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; C14:0: myristic acid; C16:0: palmitic acid; C16:1: sapienic acid; C17:0: margaric acid; C18:0: stearic acid; C18:1: oleic acid; C18:2: linoleic acid; C18:3: linolenic acid; C20:0: arachidic acid; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
Fig. 4. Biplot of world regions for the first and second principal components (SAM = South America, CNAM = Central and North America). OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel.
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
−5
0
AFRICA 5
10
15
1.0
MADAG AS C AR G AMB IA C HAD C AME R O O N EGYPT TANZ ANIA C AP E VE R DE
A DF
PC−2 (12%)
0.5
20
A DL
OIL
0
ASIA
10
20
30
1.0 CP
5
O IL
0.5
4
0
CP
−10
6
2
CL
0.0
−20
PC−2 (8%)
−10
183
0
0.0 CL
PE
−2
−0.5
−0.5
L AO S INDIA B ANG L ADE S H S R IL ANK A INDO NE S IA
−4 −1.0
−1.0 −1.0
−0.5
0.0
0.5
1.0
−1.0
−0.5
PC−1 (73%)
−15
−10
−5
0
SAM
5
0.5
1.0
PC−1 (86%)
10
15
20
−15
1.0
−10
−5
0
CNAM
5
10
15
20
1.0
0.5
PE
PE
5
CL 0.0 0
PC−2 (13%)
O IL
4
A DF
A DL
0.5
6
10
CP
PC−2 (14%)
0.0
0.0
2 0
CL
−2
OIL −0.5
−0.5 PAR AG UAY AR G E NTINA BR AZ IL
−1.0 −1.0
−0.5
0.0
−5
0.5
−5
1.0
PC−1 (68%)
CP −1.0 −1.0
−0.5
0.0
0.5
ME X IC O C OL OMB IA C OS TAR IC A NIC AR AG UA
−4 −6
1.0
PC−1 (76%)
Fig. 5. Biplot of countries within world region for the first and second principal components (SAM = South America, CNAM = Central and North America). OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell.
The negative association between oil content in seed and the ratio of shell weight to seed weight indicates a potential trait for crop improvement. The seed value of jatropha could be increased by selection and breeding for low shell weight to seed weight ratio. However, this ratio did not show the same high negative association with oil content in kernel, and therefore, the oil content in kernel will not be increased by decreasing that ratio alone significantly. Decreasing the ratio of shell weight to seed weight will certainly improve transportation efficiency and will also reduce the low digestible fraction in the press cake generated after oil extraction with screw presses. The high and negative association between oil content in kernel and protein content indicate that it would be difficult to improve those parameters simultaneously. However, increasing the size and weight of the seeds will certainly increase the absolute amount
of oil and protein in the kernel. For these traits, breeding strategies should be based on the elaboration of selection indices with appropriate economic weights for the lipid and protein fractions. The negative association between oil content in kernel and protein content might be further explained by the distribution of protein and oil storage in the seed of jatropha. A hypothesis is that as the seed increases its weight, the endosperm increases its relative proportion to total seed weight and the embryo reduces its relative contribution. To verify this hypothesis, the absolute weight of embryo and endosperm need to be measured in a panel of seeds comprising seed weight variation. The high negative association between the ash content in kernel and oil content in kernel might be explained by the composition of the sample sets investigated. In the first batch of samples, the association between seed weight measured on seed samples and
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the content of ash in kernel was much weaker than in the second batch of samples (Figs. 1 and 2). However, the overall analysis indicated a strong negative association (Fig. 3). A hypothesis explaining the association of ash content in kernel with seed weight might be based on the limit of absolute amount of ash a kernel might reach. If the maximum absolute amount of ash in kernel is a fixed threshold, the increment in kernel size will reduce the ash content in kernel. This might be further related to the lower mobility of the minerals within the plant matrix. As the kernel size increases, the oil content increases and the ash content decreased. The increment in jatropha seed production and commercialization will trigger payments based on seed quality. The utilization of mathematical models based on bulk density (i.e. seed weight determined in a vessel of known volume), number of seeds and seed moisture content (measured with a low cost and mobile device) to estimate seed quality in remote regions where immediate oil content determination is not possible should be further investigated. This fast and non-expensive method might serve as parameter for quality-based payments in commercialization channels at remote areas as well as in breeding programs without access to oil content determination with NMR equipment. Phorbol ester showed moderate associations with the traits analyzed in our study. Determination of phorbol ester will still need the development of accurate and appropriate methodologies for breeding and commercialization purposes. The methodology based on HPLC is expensive when many samples need to be processed. Recently, UV spectrophotometry (Devappa et al., 2012) and near-infrared spectroscopy (Montes et al., 2013) were investigated, but the methods need further development. Non-destructive assessment will certainly be also an important characteristic to be considered in trade and breeding. The ideal vegetable oil for biodiesel production should have low saturation and low polyunsaturation, i.e., be high in monounsaturated fatty acids (Gunstone, 2004). The negative association between C18:1 and C18:2 might be exploited to develop cultivars with high C18:1 and low C18:2. For those traits, genetic parameters such as heritability and genetic correlations need to be estimated in breeding trials to elaborate an adequate breeding strategy. The grouping of the accessions showed two contrasting groups with the accessions from AFRICA and CNAM in one group and the accessions from ASIA and SAM in other group (Fig. 4). The main traits explaining the grouping pattern were oil content in kernel and oil content in seed, with higher values in the first group (AFRICA and CNAM) than in the second group (ASIA and SAM). This result suggests that the main effects and/or interactions effects of genetic and environmental factors are more favorable for high oil content in AFRICA and CNAM than in ASIA and SAM. Similarly, the grouping of the accessions within the world regions showed that oil content in kernel and oil content in seed are the most important traits for germplasm classification. Protein content, phorbol ester content, acid detergent fiber and acid detergent lignin were of relevance in specific cases. The partitioning of trait variation is a method utilized to understand the contribution of factors to the total variation. For the material investigated in our work, the trait variation among world regions was very low with the exemption of phorbol ester and ash content in kernel. In the case of phorbol ester, the set of accessions with no phorbol ester originated all from Mexico, and this might be the cause of the different partitioning results in comparison to the other traits. Similarly, the different partitioning results for ash content in kernel are related to the geographical origin of the material but not to a single country. The establishment of efficient sampling methodologies to identify differences among accessions is crucial in plant breeding
programs. Our study indicates that measuring seed samples is a more efficient methodology than measuring traits on single seed basis. This is supported by the fact that measurements on single seeds are time consuming and have a large seed to seed variation. The methodology based on seed samples showed to be appropriate to identify differences among accessions. Domestication and early selection in jatropha is taking place and the knowledge of seed quality is important to design crossing programs for the improvement of seed quality and for the development of plant products for specific markets. Research unraveling the genetic basis of seed quality in jatropha is urgently needed. That information will contribute to design best breeding strategies and optimize the use of resources in breeding programs. Acknowledgements We thank H. Baumgärtner, B. Fischer and S. Pfeffer for the wet chemistry analysis. This work was financed by EnBW Regenwald Stiftung, Stiftung Energieforschung Baden-Württemberg, JatroSolutions GmbH and JatroSelect GmbH. References AOAC, 1990. Official Methods of Analysis, 15th edition. Association of Analytical Chemists, Washington, DC. Bernardo, R., 2002. Correlated response to selection. In: Bernardo, R. (Ed.), Breeding for Quantitative Traits in Plants. Stemma Press, Woodbury, pp. 264–266. Bates, D., Maechler, M., Bolker, B., 2012. lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0, http://CRAN.Rproject.org/package=lme4 Ceasar, S.A., Ignacimuthu, S., 2011. Applications of biotechnology and biochemical engineering for the development of jatropha and biodiesel: a review. Renewable & Sustainable Energy Reviews 15, 5176–5185. Devappa, R.K., Makkar, H.P.S., Becker, K., 2010. Optimization of conditions for the extraction of phorbol esters from Jatropha oil. Biomass and Bioenergy 34, 1125–1133. Devappa, R.K., Makkar, H.P.S., Becker, K., 2012. Localisation of nutrients and qualitative identification of toxic components in jatropha curcas seed. Journal of the Science of Food and Agriculture 92, 1519–1525. Divakara, B.N., Upadhyaya, H.D., Wani, S.P., Laxmipathi Gowda, C.L., 2010. Biology and genetic improvement of jatropha curcas L.: a review. Applied Energy 87, 732–742. Fairless, D., 2007. The little shrub that could – maybe. Nature 449, 652–655. Francis, G., Edinger, R., Becker, K., 2005. A concept for simultaneous wasteland reclamation, fuel production, and socio-economic development in degraded areas in India. Need, potential and perspectives of Jatropha plantations. Natural Resources Forum 29, 12–24. Freitas, R.G., Missio, R.F., Matos, F.S., Resende, M.D.V., Dias, L.A.S., 2011. Genetic evaluation of jatropha curcas: an important oilseed for biodiesel production. Genetics and Molecular Research 10, 1490–1498. Gunstone, F.D., 2004. Rapeseed and Canola Oil: Production, Processing, Properties and Uses. Blackwell Publishing Ltd, London. ISO, 2008. Bestimmung des Gehaltes an Gesamtstickstoff mit dem Verbrennungsverfahren nach Dumas und Berechnung des Gehaltes an Rohprotein – Teil1: Ölsaatenschote und Futtermittel (DIN EN ISO 16634-1). Kaushik, N., Kumar, K., Kumar, S., Kaushik, N., Roy, S., 2007. Genetic variability and divergence studies in seed traits and oil contant of Jatropha (Jatropha curcas L.) accessions. Biomass and Bioenergy 31, 497–502. King, A.J., He, W., Cuevas, J.A., Freundenberger, M., Ramiaramanana, D., Graham, I.A., 2009. Potential of Jatropha curcas as source of renewable oil and animal feed. Journal of Experimental Botany 60, 2897–2905. Montes, J.M., Technow, F., Bohlinger, B., Becker, K., 2013. Grain quality determination by means of near infrared spectroscopy in Jatropha curcas L. Industrial Crops and Products 43, 301–305. Ovando-Medina, I., Espinosa-García, F.J., Núnez-Farfán, J., Salvador-Figueroa, M., 2011. Genetic variation in Mexican Jatropha curcas L. Estimated with seed oil fatty acids. Journal of Oleo Science 60, 301–311. R Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org/ Rao, G.R., Korwar, G.R., Shanker, A.K., Ramakrishna, Y.S., 2008. Genetic associations, variability and diversity in seed characters, growth, reproductive phenology and yield of Jatropha curcas (L.) accessions. Trees 22, 697–709. Rincon, F., Johnson, B., Crossa, J., Taba, S., 1996. Cluster analysis, an approach to sampling variability in maize accessions. Maydica 41, 307–316.
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185 Sanderson, K., 2009. Wonder weed plans fail to flourish. Nature 461, 328–329. Shabanimofrad, M., Rafiia, M.Y., Megat Wahaba, P.E., Biabania, A.R., Latif, M.A., 2013. Phenotypic, genotypic and genetic divergence found in 48 newly collected Malaysian accessions of Jatropha curcas L. Industrial Crops and Products 42, 543–551.
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Industrial Crops and Products journal homepage: www.elsevier.com/locate/indcrop
Seed quality diversity, trait associations and grouping of accessions in Jatropha curcas L. Juan M. Montes a,∗ , Frank Technow b , Brigitte Bohlinger c,d , Klaus Becker a,c,d a
JatroSelect GmbH, Echterdinger Str. 30, 70599 Stuttgart, Germany Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Fruwirthstr. 21, 70599 Stuttgart, Germany Institute of Animal Production in the Tropics and Subtropics, University of Hohenheim, Fruwirthstr. 12, 70593 Stuttgart, Germany d JatroSolutions GmbH, Echterdinger Str. 30, 70599 Stuttgart, Germany b c
a r t i c l e
i n f o
Article history: Received 28 May 2013 Received in revised form 13 August 2013 Accepted 15 August 2013 Keywords: Jatropha Seed quality Kernel Shell Germplasm Breeding
a b s t r a c t Jatropha curcas L. (jatropha) has recently received great attention for its utilization in biofuel production, rehabilitation of wasteland and rural development. Improvement of seed quality is an important breeding goal but jatropha seed quality has been investigated with sample sets comprising a small number of traits, accessions and environments. Our main goal was to investigate a large number of traits in a wide and geographical diverse collection of seed samples to have a comprehensive view on the phenotypic variation of seed quality in jatropha. Our objectives were to (i) assess phenotypic variation of jatropha seed quality traits, (ii) investigate the association among those traits (iii) group germplasm and (iv) examine the partition of the trait variation attributed to factors of geographical origin (world regions and countries) and sampling procedures (single seeds vs. seed samples). Phenotypic variation was larger than reported previously. We detected a strong positive association between seed weight and the contents of oil in seed and kernel. Oil content in seed was negatively associated with the ratio of shell weight to seed weight. Contents of oil and protein in kernel were also associated negatively. Accessions from Africa and Central and North America grouped together and separated of accessions from Asia and South America. Countries and accessions within countries contributed most to the total variance of seed quality traits. Determination of seed quality among accessions using seed samples is more efficient than using single seeds. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Jatropha curcas L. (jatropha) is an important undomesticated plant which has received great attention in recent years for its utilization in biodiesel production (Ceasar and Ignacimuthu, 2011). JCL is a small tree or large shrub, which belongs to the Euphorbiaceae family and has a life expectancy of up to 50 years (Divakara et al., 2010). The plant has a high potential for greening and rehabilitation of wastelands and the seeds have a high oil concentration with excellent quality for conversion into biodiesel (Francis et al., 2005). Although various efforts have been made to develop jatropha as an industrial crop (Fairless, 2007; Sanderson, 2009), the absence of improved cultivars and lack of agronomic knowledge represent the main bottleneck that limits the full exploitation of this plant’s potential (King et al., 2009). Improvement of seed quality is an important breeding goal but seed quality diversity in jatropha has been investigated with
∗ Corresponding author. Tel.: +49 0711 459 99771; fax: +49 0711 459 99789. E-mail address: [email protected] (J.M. Montes). 0926-6690/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.indcrop.2013.08.046
datasets comprising a small number of traits, accessions and environments (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). A more comprehensive assessment of phenotypic variation of seed quality can be performed by analysis of larger datasets including additional quality traits and a larger number of accessions collected in different climatic regions where jatropha grows predominately. Plant breeders usually exploit the associations among traits to optimize breeding programs by means of indirect selection procedures (Bernardo, 2002). In jatropha, a positive linear association between oil content and seed weight was reported (Rao et al., 2008) and associations among contents of fatty acids were investigated (Ovando-Medina et al., 2011). Expanding the matrix of traits to important quality traits such as contents of protein and phorbol ester (the chemical compound causing toxicity), might reveal new trait associations useful to increase efficiency in breeding programs. Knowledge of common characteristics of individuals can assist breeders to identify germplasm sources which have potential utility for specific breeding objectives (Rincon et al., 1996). Besides, cluster analysis based on phenotypic data might assist to identify
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185 Table 1 Geographical origin and sampling structure of the 370 jatropha seed samples utilized in this study. Sample code
N
Country
World region
JCL001-038 JCL039-043 JCL044-066 JCL067-069 JCL070 JCL071-076 JCL077-084 JCL085-086 JCL087-093 JCL094-095 JCL096-125 JCL126 JCL127-139 JCL140-291 JCL292-330 JCL331 JCL332-368 JCL369 JCL370
38 5 23 3 1 6 8 2 7 2 30 1 13 152b 39 1 37 1 1
Argentina Bangladesh Brazil Cameroon Cape Verde Chad Colombiaa Costa Rica Egypt Gambia India Indonesia Laos Madagascar Mexico Nicaragua Paraguay Sri Lanka Tanzania
SAM ASIA SAM AFRICA AFRICA AFRICA CNAM CNAM AFRICA AFRICA ASIA ASIA ASIA AFRICA CNAM CNAM SAM ASIA AFRICA
a
Seeds originated from Salvador planted in Colombia. 102 samples are from 34 accessions sampled in triplicate. The rest of the samples corresponded to single accessions. SAM = South America; CNAM = Central and North America.
179
only. The traits measured on single seed basis were seed weight, kernel weight, shell weight, seed length, seed width and seed thickness. The fatty acids profile was measured in the second batch only: myristic acid (C14:0), palmitic acid (C16:0), sapienic acid (C16:1), margaric acid (C17:0), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), linolenic acid (C18:3), arachidic acid (C20:0). The following traits were measured on both batches: oil content in seeds, seed weight, oil content in kernel, protein content in kernel, content of phorbol ester in kernel, ash content in kernel, acid detergent fiber content in shells, acid detergent lignin content in shells and ash content in shells. 2.2. Measurements of single seeds A total of 20 seeds per sample were individually measured. The length, width and thickness (distance from standing point to highest point of a free standing seed on a table) and seed weight were measured firstly. The shell and kernel of each seed were then separated and their weights were measured. The proportion of shell in seed was calculated as the ratio of shell weight to seed weight. The total number of single seed measurements was 3000.
b
geographical regions where genetic and/or environmental factors are favoring seed quality. A more comprehensive assessment of seed quality diversity in jatropha need to be performed with datasets comprising a large number of quality traits in a large number of geographical diverse accessions. Despite the various studies on phenotypic variation carried out, the conclusions from them are limited to the materials investigated which in most cases are of local relevance (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). Our main goal was to establish a wide collection of seed samples with a significant number of accessions from different origins (Africa, Asia and America) to have a comprehensive view on the phenotypic variation in jatropha. To our knowledge, the germplasm collection analyzed in this study comprises the largest collection (302 accessions) reported till present. Our objectives were to (i) assess the phenotypic variation of jatropha seed quality traits, (ii) investigate the association among those traits, (iii) group germplasm to identify accessions which have potential utility for specific breeding purposes and (iv) examine the partition of the trait variation attributed to factors of geographical origin (world regions and countries) and sampling procedures (single seeds vs. seed samples). 2. Materials and methods 2.1. Plant material and seed traits Seed samples were taken from JatroSelect GmbH germplasm bank. The seeds were collected from single trees during collection travels in the time period between 2008 and 2010. The amount of seed available for each accession in the bank ranged between 0.2 and 0.5 kg. A total of 302 accessions were selected from the germplasm bank and seed samples of 40 seeds were taken from the stored seeds of each accession. To assess the variation among seed samples harvested within a single tree, 34 accessions were sampled in triplicate. The final sample set comprised a total of 370 seed samples that originated from a wide range of geographical regions worldwide (Table 1). The samples were processed in two batches. The first batch comprised 150 samples and the second batch comprised 220 samples. Single seed measurements were performed in the first batch
2.3. Oil content and seed weight in seeds The content of oil in seeds was determined with seed analyzer minispec mq7.5 (Bruker Optik GmbH, Rheinstetten, Germany) based on nuclear magnetic resonance (NMR). Seed weight was calculated as the weight of the seed sample divided by 40 (the number of seeds in the sample). 2.4. Wet chemistry analysis of kernel and shell fractions The seed samples were de-shelled and the resulting kernel and shell fractions were analyzed separately. Kernels and shells were ground using a IKA-A11 basic grinder to 1.5 mm (kernel) and 1 mm (shell) particle size. The ground materials were subsequently used for wet chemistry analysis. For the kernel fraction, the oil content was determined with petroleum ether in a Tecator Soxhlet type extractor for 6 h until the evaporation of residual ether revealed that it contained <0.2% lipid. The oil concentration of the samples was determined gravimetrically (AOAC, 1990). The nitrogen content was determined using a C/N-Macro-Analyzer (C/N vario MAX, Elementar Analysensysteme, Hanau, Germany) according to the Dumas principle. The protein content was calculated as nitrogen content × 6.25 (ISO 16634-1: 2008). Concentration of phorbol ester was determined in at least two duplicates of each sample (Devappa et al., 2010). Separation and identification of fatty acid methyl esters (FAMEs) were carried out in at least two duplicates of each sample by the boron trifluoride method (AOAC, 1990). Content of ash in kernel was determined by difference in weight before and after incineration in a muffle furnace at 500 ◦ C for 6 h (AOAC, 1990). For the shell fraction, acid detergent fiber and acid detergent lignin were determined (Van Soest et al., 1991). Content of ash in shells was determined by following the same procedure as for the determination of content of ash in kernel. All values were expressed on dry matter basis. 2.5. Statistical analysis All computations were performed within the R statistical environment (R Core Team, 2012). Descriptive statistics (mean, minimum, maximum and phenotypic coefficient of variation) were calculated for all traits analyzed. Trait associations were assessed by Spearman rank correlation to avoid scale-dependent correlations. The grouping structure of the material was investigated with
yijk = ˇ0 + Ri + C/Rij + G/C/Rijk
(1)
where yijk is a phenotypic observation made on the kth accession within the jth country of the ith world region, ˇ0 is a common intercept, Ri is the effect of the ith world region, C/Rij the effect of the jth country within the ith world region and G/C/Rij the effect of the kth accession from the jth country in world region i, which is confounded with the residual. All effects but ˇ0 were taken as random. The following linear mixed model was used to investigate the variance components for accessions, seed samples and single seeds with data of the 34 accessions sampled in triplicate: yijk = ˇ0 + Gi + S/Gij + K/S/Gijk
(2)
where yijk is a phenotypic observation made on the kth seed within the jth seed sample of the ith accession, ˇ0 is a common intercept, Gi is the effect of ith accession, S/Gij the effect of the jth seed sample of the ith accesion and K/S/Gijk the effect of the kth seed within the jth seed sample of the ith accession, again, confounded with the residual. All effects but ˇ0 were taken as random. For the traits where no single seed measurement were available, the model was reduced as below, yijk = ˇ0 + Gi + S/Gij
SW1 KW
ASHS
ADL
ADF
ASHK
CP
PE
OIL
CL
SSP
S W2
ST
SL
S WD
a principal component analysis (PCA), using trait data in common to all 302 accessions. The PCA was conducted across world regions and separately for each. All variables were scaled to unit variance and centered to mean zero. We used R function “princomp” for computing the principal components. We investigated the variance components for world regions, countries and accessions by using data of the first and second batches jointly and the following model:
KW
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
SW
180
1
0.8
SW 0.6
SL S WD
0.4
ST 0.2
SSP SW2
0
OIL −0.2
CL CP
−0.4
PE −0.6
ASHK ADF
−0.8
ADL 1
Fig. 1. Correlation matrix of traits measured in 150 seed samples of jatropha. Traits measured on single seeds: SW1: seed weight; KW: kernel weight; SW: shell weight; SL: seed length; SWD: seed width; ST: seed thickness; SSP: proportion of shell in seed. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell (%).
(3)
where S/Gij now also contains variation between single seeds as well as residual variation. We used the R mixed model package “lme4” (Bates et al., 2012) for fitting the models with REML. For straightforward interpretation, the variance components were expressed as percentage of the total variance. 3. Results The sample collection comprised a large number of accessions (302) from four world regions and 19 countries where jatropha grows predominantly (Table 1). The collection comprised 104 accessions from Africa, 50 from Asia, 50 from Central and North America and 98 from South America. This resulted in a geographical contribution for the world regions of 34% for Africa (AFRICA), 17% for Asia (ASIA), 17% for Central and North America (CNAM) and 32% for South America (SAM). Seed quality variation measured on single seeds was high for seed weight, kernel weight, shell weight and the ratio of shell weight to seed weight and low for seed length, seed width and seed thickness (Table 2). For the traits measured on seed samples, the variation was high for seed weight, oil content in seed, phorbol ester content, ash content in kernel, C16:1, C18:0, C18:3, C20:0 and ash content in shell; intermediate for oil content in kernel, protein content in kernel, C18:1 and C18:2; and low for C16:0, acid detergent fiber content and acid detergent lignin content. The traits C14:0 and C17:0 showed an extremely high variation due to the presence of highly contrasting values in the sample sets. The correlation matrix of traits in the first batch of samples revealed a high positive association of seed weight in single seeds with kernel weight, shell weight, seed length, seed thickness and seed weight in seed samples (Fig. 1). A high and negative association was found between the ratio of shell weight to seed weight
and oil content in seed. Oil content in seed and oil content in kernel were highly positively associated and the protein content was highly negative associated with oil content in seed and oil content in kernel. The ash content in kernel was highly negatively associated with the oil content in seed and oil content in kernel. The traits acid detergent fiber and acid detergent lignin showed a high positive association. The correlation matrix of traits in the second batch of samples confirmed the positive association of seed weight with oil content in seed and oil content in kernel as well as the association between oil content in seed and oil content in kernel (Fig. 2). The negative association between protein content with oil content in kernel and oil content in seed was also confirmed. A high negative association was observed between seed weight and ash content in kernel, which was not observed for the first batch of samples. High and negative associations were observed between C18:3 and seed weight, oil content in seed and oil content in kernel. The traits C18:1 and C18:2 showed a high and negative association. There was a positive association between acid detergent fiber and acid detergent lignin. The combined analysis of trait associations with both batches of samples showed a high positive association of seed weight with oil content in seed and oil content in kernel and between oil content in seed and oil content in kernel (Fig. 3). A high negative association between oil content in kernel and protein content was observed and to a lesser extent of the latter with oil content in seed. The ash content in kernel showed a high negative association with oil content in seed, oil content in kernel and seed weight. Other trait associations were weaker. The PCA of the world regions resulted in two general groups (Fig. 4). One group comprised predominantly accessions from AFRICA and CNAM, the other group accessions from ASIA and SAM. The main traits affecting the first principal component were oil content in seed and oil content in kernel. The analysis
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
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Table 2 Descriptive statistics of traits measured on single seeds and seed samples of jatropha. Material
Trait
Mean
Min
Max
Single seeds
Seed weight (g) Kernel weight (g) Shell weight (g) Seed length (cm) Seed width (cm) Seed thickness (cm) Shell to seed ratio
0.6 0.4 0.2 1.7 1.1 0.8 36.4
0.1 0.0 0.1 1.1 0.8 0.6 24.6
0.9 0.6 0.4 2.3 1.6 1.1 100.0
CV 18.6 22.4 16.6 7.0 5.4 7.0 19.9
Seed samples
Seed weight (g) Oil (%)
0.6 31.7
0.3 10.6
0.8 41.0
17.9 19.1
Kernel
Oil (%) Protein (%) Phorbol ester (mg g−1 ) Ash (%) C14:0 (%) C16:0 (%) C16:1 (%) C17:0 (%) C18:0 (%) C18:1 (%) C18:2 (%) C18:3 (%) C20:0 (%)
53.5 27.5 4.6 4.9 0.0 14.0 0.9 0.0 5.3 40.3 39.0 0.2 0.2
28.5 18.4 0.0 2.5 0.0 10.8 0.4 0.0 3.5 29.0 20.0 0.1 0.0
65.6 37.7 10.3 9.0 0.3 22.0 2.3 1.9 17.7 54.4 50.0 0.5 0.3
12.7 13.1 55.6 22.4 194.3 8.2 24.5 503.7 29.6 10.9 13.8 29.9 19.1
Shell
Acid detergent fiber (%) Acid detergent lignin (%) Ash (%)
76.5 41.0 4.4
69.2 20.7 2.2
87.2 48.3 8.7
3.3 5.5 22.4
C14:0: myristic acid; C16:0: palmitic acid; C16:1: sapienic acid; C17:0: margaric acid; C18:0: stearic acid; C18:1: oleic acid; C18:2: linoleic acid; C18:3: linolenic acid and C20:0: arachidic acid.
within AFRICA resulted in one major group of accessions from Madagascar grouped together and a few accessions from Egypt, Cameroon and Chad that grouped separately. The analysis within ASIA showed three groups formed with accessions from Laos, India and Bangladesh. The analysis of SAM revealed a larger distance between accessions from Brazil and Paraguay with the accession from Argentina forming a bridge between them. The analysis of CNAM revealed that accession from Mexico grouped together with those from Costa Rica and accessions from Colombia grouping together with an accession from Nicaragua. In all cases, the main traits affecting the first principal component were oil content in seed and oil content in kernel (Table S1). Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.indcrop.2013.08.046. The variance component analysis showed that the larger fraction of the variation is present among countries within a world region and accessions within a country (Table 3). The variance component analysis of the traits analyzed on single seeds revealed a high seed to seed variation (Table 4). The traits analyzed in seed samples resulted in a larger fraction of the variation attributed
to accessions in comparison to the single seed measurements (Fig. 5). 4. Discussion Characterization and grouping of germplasm, knowledge of trait associations and appropriate sampling procedures are important aspects to be considered in breeding programs. Most of the studies performed till present comprised a reduced number of quality traits examined in a low number of accessions or the accessions were collected in a narrow geographical area (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013). This situation limits a comprehensive assessment of the seed quality diversity in jatropha. The seed quality variation observed in our panel of 302 accessions was larger than the variation observed in previous studies (Kaushik et al., 2007; Rao et al., 2008; Sunil et al., 2008; Freitas et al., 2011; Shabanimofrad et al., 2013) for all the traits investigated. The larger phenotypic variation in our study might be explained by the inclusion of a larger and more diverse panel of jatropha accessions comprising larger genetic and environmental effects.
Table 3 Percentage of the total variance attributed to the variation among world regions, countries within world regions and accessions within countries and world regions (which is confounded with the residuals) for seed quality traits of 302 jatropha accessions. Variation Among world regions Among countries within world regions Among accessions within countries and world regions
Df
Seed weight
Oil content in seed
Oil content in kernel
Protein content in kernel
Phorbol ester in kernel
Ash content in kernel
Acid detergent fiber in shell
Acid detergent lignin in shell
Ash content in shell
3
0.0
0.0
0.0
0.0
14.6
18.8
0.4
2.5
7.6
15
70.6
70.0
69.3
50.5
34.7
44.2
42.4
29.8
9.6
283
29.4
30.0
30.7
49.5
50.7
37.0
57.2
67.7
82.8
Df: degree of freedom (approximate values returned by R function “aov”).
182
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Table 4 Percentage of the total variance attributed to the variation among accessions, seed samples within accessions and single seeds within seed samples and accessions (which is confounded with the residual) for seed quality of 34 jatropha accessions sampled in triplicate. Variation
Df
SW1
KW
SW
SL
ST
SWD
SSP
SW2
OIL
CL
CP
PE
ASHK
ADF
ADL
ASHS
Accessions Seed samples Single seeds
33 68 1938
32.8 0.0 67.2
21.9 0.0 78.1
55.7 0.0 44.3
34.8 0.3 65.0
30.8 0.0 69.2
36.1 0.0 63.9
5.1 0.0 94.9
93.1 6.9
91.6 8.5
92.6 7.4
95.3 4.7
93.3 6.7
89.1 10.9
61.6 38.4
47.7 52.3
70.0 30.0
ASHS
ADL
ADF
ASHK
PE
CP
CL
The quantification of the trait variation attributed to genetic and environmental factors cannot be performed with the data available in our study. Those studies need to be performed with seeds harvested from field experiments that have been managed similarly in terms of harvest and post-harvest procedures. Such studies will reveal the genetic and environmental basis of important quality traits. The analysis of trait associations in the first batch of samples showed a positive association among seed weight measured on single seed basis, seed weight measured on seed samples, kernel weight, shell weight, seed length, seed width and seed thickness. It is expected that those traits are related by dimensions of form and weight (Fig. 1). The high association between seed weight measured on single seed basis and seed weight measured in seed samples indicates that the single seed weight can be measured on group of seeds more efficiently than measuring single seeds. Similarly, the high association between oil content in seed and oil content in kernel indicated that measures collected on the group of seeds with the NMR equipment is the best method to determine oil content in seeds and indirectly in kernels. By using NMR methodology, seed weight, oil content in seed and moisture content can be measured very efficiently in one step.
OIL
Df: degree of freedom; traits measured on single seeds basis: SW1: seed weight; KW: kernel weight; SW: shell weight; SL: seed length; SWD: seed width; ST: seed thickness; SSP: proportion of shell in seed; traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
1
S W2 0.8
OIL
0.6
0.4
CL
0.2
CP
0
PE −0.2
ASHK
−0.4
−0.6
ADF
ADL
−0.8
S W2 OIL
ASHS
ADF
ADL
C20_0
C18_3
C18_1
C18_2
C17_0
C18_0
C16_1
C14_0
C16_0
ASHK
PE
CL
CP
OIL
−1
1
Fig. 3. Correlation matrix of traits measured in 370 seed samples of jatropha. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
0.8
CL CP
0.6
PE 0.4
ASHK C14_0
0.2
C16_0 C16_1
0
C17_0 −0.2
C18_0 C18_1
−0.4
C18_2 C18_3
−0.6
C20_0 −0.8
ADF ADL
−1
Fig. 2. Correlation matrix of traits measured in 220 seed samples of jatropha. Traits measured on seed samples: SW2: seed weight; OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ASHK: ash content in kernel; C14:0: myristic acid; C16:0: palmitic acid; C16:1: sapienic acid; C17:0: margaric acid; C18:0: stearic acid; C18:1: oleic acid; C18:2: linoleic acid; C18:3: linolenic acid; C20:0: arachidic acid; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell and ASHS: ash content in shell.
Fig. 4. Biplot of world regions for the first and second principal components (SAM = South America, CNAM = Central and North America). OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel.
J.M. Montes et al. / Industrial Crops and Products 51 (2013) 178–185
−5
0
AFRICA 5
10
15
1.0
MADAG AS C AR G AMB IA C HAD C AME R O O N EGYPT TANZ ANIA C AP E VE R DE
A DF
PC−2 (12%)
0.5
20
A DL
OIL
0
ASIA
10
20
30
1.0 CP
5
O IL
0.5
4
0
CP
−10
6
2
CL
0.0
−20
PC−2 (8%)
−10
183
0
0.0 CL
PE
−2
−0.5
−0.5
L AO S INDIA B ANG L ADE S H S R IL ANK A INDO NE S IA
−4 −1.0
−1.0 −1.0
−0.5
0.0
0.5
1.0
−1.0
−0.5
PC−1 (73%)
−15
−10
−5
0
SAM
5
0.5
1.0
PC−1 (86%)
10
15
20
−15
1.0
−10
−5
0
CNAM
5
10
15
20
1.0
0.5
PE
PE
5
CL 0.0 0
PC−2 (13%)
O IL
4
A DF
A DL
0.5
6
10
CP
PC−2 (14%)
0.0
0.0
2 0
CL
−2
OIL −0.5
−0.5 PAR AG UAY AR G E NTINA BR AZ IL
−1.0 −1.0
−0.5
0.0
−5
0.5
−5
1.0
PC−1 (68%)
CP −1.0 −1.0
−0.5
0.0
0.5
ME X IC O C OL OMB IA C OS TAR IC A NIC AR AG UA
−4 −6
1.0
PC−1 (76%)
Fig. 5. Biplot of countries within world region for the first and second principal components (SAM = South America, CNAM = Central and North America). OIL: oil content in seed; CL: oil content in kernel; CP: protein content in kernel; PE: phorbol ester content in kernel; ADF: acid detergent fiber content in shell; ADL: acid detergent lignin content in shell.
The negative association between oil content in seed and the ratio of shell weight to seed weight indicates a potential trait for crop improvement. The seed value of jatropha could be increased by selection and breeding for low shell weight to seed weight ratio. However, this ratio did not show the same high negative association with oil content in kernel, and therefore, the oil content in kernel will not be increased by decreasing that ratio alone significantly. Decreasing the ratio of shell weight to seed weight will certainly improve transportation efficiency and will also reduce the low digestible fraction in the press cake generated after oil extraction with screw presses. The high and negative association between oil content in kernel and protein content indicate that it would be difficult to improve those parameters simultaneously. However, increasing the size and weight of the seeds will certainly increase the absolute amount
of oil and protein in the kernel. For these traits, breeding strategies should be based on the elaboration of selection indices with appropriate economic weights for the lipid and protein fractions. The negative association between oil content in kernel and protein content might be further explained by the distribution of protein and oil storage in the seed of jatropha. A hypothesis is that as the seed increases its weight, the endosperm increases its relative proportion to total seed weight and the embryo reduces its relative contribution. To verify this hypothesis, the absolute weight of embryo and endosperm need to be measured in a panel of seeds comprising seed weight variation. The high negative association between the ash content in kernel and oil content in kernel might be explained by the composition of the sample sets investigated. In the first batch of samples, the association between seed weight measured on seed samples and
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the content of ash in kernel was much weaker than in the second batch of samples (Figs. 1 and 2). However, the overall analysis indicated a strong negative association (Fig. 3). A hypothesis explaining the association of ash content in kernel with seed weight might be based on the limit of absolute amount of ash a kernel might reach. If the maximum absolute amount of ash in kernel is a fixed threshold, the increment in kernel size will reduce the ash content in kernel. This might be further related to the lower mobility of the minerals within the plant matrix. As the kernel size increases, the oil content increases and the ash content decreased. The increment in jatropha seed production and commercialization will trigger payments based on seed quality. The utilization of mathematical models based on bulk density (i.e. seed weight determined in a vessel of known volume), number of seeds and seed moisture content (measured with a low cost and mobile device) to estimate seed quality in remote regions where immediate oil content determination is not possible should be further investigated. This fast and non-expensive method might serve as parameter for quality-based payments in commercialization channels at remote areas as well as in breeding programs without access to oil content determination with NMR equipment. Phorbol ester showed moderate associations with the traits analyzed in our study. Determination of phorbol ester will still need the development of accurate and appropriate methodologies for breeding and commercialization purposes. The methodology based on HPLC is expensive when many samples need to be processed. Recently, UV spectrophotometry (Devappa et al., 2012) and near-infrared spectroscopy (Montes et al., 2013) were investigated, but the methods need further development. Non-destructive assessment will certainly be also an important characteristic to be considered in trade and breeding. The ideal vegetable oil for biodiesel production should have low saturation and low polyunsaturation, i.e., be high in monounsaturated fatty acids (Gunstone, 2004). The negative association between C18:1 and C18:2 might be exploited to develop cultivars with high C18:1 and low C18:2. For those traits, genetic parameters such as heritability and genetic correlations need to be estimated in breeding trials to elaborate an adequate breeding strategy. The grouping of the accessions showed two contrasting groups with the accessions from AFRICA and CNAM in one group and the accessions from ASIA and SAM in other group (Fig. 4). The main traits explaining the grouping pattern were oil content in kernel and oil content in seed, with higher values in the first group (AFRICA and CNAM) than in the second group (ASIA and SAM). This result suggests that the main effects and/or interactions effects of genetic and environmental factors are more favorable for high oil content in AFRICA and CNAM than in ASIA and SAM. Similarly, the grouping of the accessions within the world regions showed that oil content in kernel and oil content in seed are the most important traits for germplasm classification. Protein content, phorbol ester content, acid detergent fiber and acid detergent lignin were of relevance in specific cases. The partitioning of trait variation is a method utilized to understand the contribution of factors to the total variation. For the material investigated in our work, the trait variation among world regions was very low with the exemption of phorbol ester and ash content in kernel. In the case of phorbol ester, the set of accessions with no phorbol ester originated all from Mexico, and this might be the cause of the different partitioning results in comparison to the other traits. Similarly, the different partitioning results for ash content in kernel are related to the geographical origin of the material but not to a single country. The establishment of efficient sampling methodologies to identify differences among accessions is crucial in plant breeding
programs. Our study indicates that measuring seed samples is a more efficient methodology than measuring traits on single seed basis. This is supported by the fact that measurements on single seeds are time consuming and have a large seed to seed variation. The methodology based on seed samples showed to be appropriate to identify differences among accessions. Domestication and early selection in jatropha is taking place and the knowledge of seed quality is important to design crossing programs for the improvement of seed quality and for the development of plant products for specific markets. Research unraveling the genetic basis of seed quality in jatropha is urgently needed. That information will contribute to design best breeding strategies and optimize the use of resources in breeding programs. Acknowledgements We thank H. Baumgärtner, B. Fischer and S. Pfeffer for the wet chemistry analysis. This work was financed by EnBW Regenwald Stiftung, Stiftung Energieforschung Baden-Württemberg, JatroSolutions GmbH and JatroSelect GmbH. References AOAC, 1990. Official Methods of Analysis, 15th edition. Association of Analytical Chemists, Washington, DC. Bernardo, R., 2002. Correlated response to selection. In: Bernardo, R. (Ed.), Breeding for Quantitative Traits in Plants. Stemma Press, Woodbury, pp. 264–266. Bates, D., Maechler, M., Bolker, B., 2012. lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0, http://CRAN.Rproject.org/package=lme4 Ceasar, S.A., Ignacimuthu, S., 2011. Applications of biotechnology and biochemical engineering for the development of jatropha and biodiesel: a review. Renewable & Sustainable Energy Reviews 15, 5176–5185. Devappa, R.K., Makkar, H.P.S., Becker, K., 2010. Optimization of conditions for the extraction of phorbol esters from Jatropha oil. Biomass and Bioenergy 34, 1125–1133. Devappa, R.K., Makkar, H.P.S., Becker, K., 2012. Localisation of nutrients and qualitative identification of toxic components in jatropha curcas seed. Journal of the Science of Food and Agriculture 92, 1519–1525. Divakara, B.N., Upadhyaya, H.D., Wani, S.P., Laxmipathi Gowda, C.L., 2010. Biology and genetic improvement of jatropha curcas L.: a review. Applied Energy 87, 732–742. Fairless, D., 2007. The little shrub that could – maybe. Nature 449, 652–655. Francis, G., Edinger, R., Becker, K., 2005. A concept for simultaneous wasteland reclamation, fuel production, and socio-economic development in degraded areas in India. Need, potential and perspectives of Jatropha plantations. Natural Resources Forum 29, 12–24. Freitas, R.G., Missio, R.F., Matos, F.S., Resende, M.D.V., Dias, L.A.S., 2011. Genetic evaluation of jatropha curcas: an important oilseed for biodiesel production. Genetics and Molecular Research 10, 1490–1498. Gunstone, F.D., 2004. Rapeseed and Canola Oil: Production, Processing, Properties and Uses. Blackwell Publishing Ltd, London. ISO, 2008. Bestimmung des Gehaltes an Gesamtstickstoff mit dem Verbrennungsverfahren nach Dumas und Berechnung des Gehaltes an Rohprotein – Teil1: Ölsaatenschote und Futtermittel (DIN EN ISO 16634-1). Kaushik, N., Kumar, K., Kumar, S., Kaushik, N., Roy, S., 2007. Genetic variability and divergence studies in seed traits and oil contant of Jatropha (Jatropha curcas L.) accessions. Biomass and Bioenergy 31, 497–502. King, A.J., He, W., Cuevas, J.A., Freundenberger, M., Ramiaramanana, D., Graham, I.A., 2009. Potential of Jatropha curcas as source of renewable oil and animal feed. Journal of Experimental Botany 60, 2897–2905. Montes, J.M., Technow, F., Bohlinger, B., Becker, K., 2013. Grain quality determination by means of near infrared spectroscopy in Jatropha curcas L. Industrial Crops and Products 43, 301–305. Ovando-Medina, I., Espinosa-García, F.J., Núnez-Farfán, J., Salvador-Figueroa, M., 2011. Genetic variation in Mexican Jatropha curcas L. Estimated with seed oil fatty acids. Journal of Oleo Science 60, 301–311. R Core Team, 2012. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org/ Rao, G.R., Korwar, G.R., Shanker, A.K., Ramakrishna, Y.S., 2008. Genetic associations, variability and diversity in seed characters, growth, reproductive phenology and yield of Jatropha curcas (L.) accessions. Trees 22, 697–709. Rincon, F., Johnson, B., Crossa, J., Taba, S., 1996. Cluster analysis, an approach to sampling variability in maize accessions. Maydica 41, 307–316.
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