Identifying Superior Rainfed Barley Genotypes in Farmers' Fields Using Participatory Varietal Selection

Transcription

1 J. Crop Sci. Biotech (December) 14 (4) : 281 ~ 288 DOI No /s RESEARCH ARTICLE Identifying Superior Rainfed Barley Genotypes in Farmers' Fields Using Participatory Varietal Selection Reza Mohammadi 1 *, Kouresh Nader Mahmoodi 1, Reza Haghparast 1, Stefania Grando 2, Maryam Rahmanian 3, Salvatore Ceccarelli 2 1 Dryland Agricultural Research Institute, P O Box , Kermanshah, Iran 2 International Centre for Agricultural Research in the Dry Areas (ICARDA), P O Box 5466, Aleppo, Syria 3 Centre for Sustainable Development (CENESTA), Tehran, Iran Received: November 8, 2010 / Revised: July 28, 2011 / Accepted: September 14, 2011 Korean Society of Crop Science and Springer 2011 Abstract This study was carried out to identify superior barley genotypes for the rainfed areas of western Iran using a participatory varietal selection (PVS) approach. Three field experiments were conducted in two randomly selected farmers' fields and in one rainfed research station in the cropping season with 69 genotypes (including one local and one improved check). Several univariate and multivariate methods were used to analyze qualitative (farmers' scores) and quantitative (grain yield) data. Individual farmers' scores in each village were positively correlated, indicating that the farmers tended to discriminate genotypes in similar fashion, although the genotypes actually selected by farmers were different in the two villages. In recent years, a greater number of farmers in western Iran preferred the improved variety (Sararood-1) over the local barley (Mahali), while in this project the farmers preferred the new genotypes over the two checks. This was also verified by the quantitative data showing that the checks were outyielded by the new genotypes. Farmers were efficient in identifying the best genotypes for their specific environment, as shown by biplot analysis, indicating their competence in selection. The genotypes selected by the breeder and farmers were almost similar but some differences existed. In conclusion, PVS is a powerful way to involve farmers for selecting and testing new cultivars that are adapted to their needs, systems and environments. Key words: barley, farmers, participatory varietal selection, quantitative and qualitative data Introduction There has been a limited adoption of improved varieties and agricultural technologies by smallholder farmers in many regions of the world. According to the participatory research literature, one of the major factors limiting adoption has been insufficient attention to farmers priorities and perceptions while developing and releasing cultivars (Ashby and Sperling 1995; Chambers et al. 1989). Researchers and extension staff are often aware that farmers need to be consulted and that indigenous knowledge should be taken into account while planning research programs; however the time and resources required for participatory research are often seen as onerous (Snapp et al. 2002). The centralized plant breeding techniques of the green revolution have yielded good results in more favorable agricultural Reza Mohammadi ( ) rmohammadi95@yahoo.com environments. However, most low-resource farmers in marginal areas have not benefited from these varieties. As an alternative to centralized breeding, farmer participatory approaches using participatory varietal selection (PVS) and participatory plant breeding (PPB) can be used. PPB is an extension of PVS. In PPB, farmers are actively involved in the breeding process, from setting goals to selecting variable, early generation material. In PVS, farmers are given a wide range of new cultivars to test for themselves in their own fields. In PPB programs, the results of PVS were exploited by using identified cultivars as parents of crosses. Participatory research, which allows farmers, research scientists and extension agents to conduct research together, is essential particularly for identifying the traits preferred by farmers. Farmers fields provide a multitude of diverse environments which allow exploiting genotype-by-environment interaction The Korean Society of Crop Science

2 282 Identifying Superior Rainfed Barley Genotypes Using PVS effect between the farmers fields and research stations given that in most cases, and particularly under semi-arid conditions, they are different (Ceccarelli and Grando 2007). In addition, farmers may use different selection criteria from those used by the breeders. Conventional plant breeding (CPB) has been successful for farmers in high potential environments because they can afford those external inputs generally required by these varieties it results in. However, it did not make good progress in marginal environments because such environments are highly diverse and farmers are generally poor, hence they cannot afford expensive inputs including certified seed (Almekinders and Elings 2001; Byerlee and Husain 1993; Ceccarelli and Grando 2007; Morris and Bellon 2004). In CPB, initial selection in large populations of breeding materials is conducted on optimally managed research stations by breeders. Barley varieties developed under the optimum agronomic and environmental conditions in CPB usually are not widely adopted by farmers in the marginal environmental and economic conditions under which the crop is actually grown (Abay and Bjornstad 2008; Ceccarelli 1994, 1996). Breeding crops adapted to specific farming systems and ecological zones is important for these systems, and will require decentralized breeding programs that can address the needs of a diversity of agronomic and social environments. For this to be successful, it is essential to have farmers actively participating in the research process. However, in Iran very few barley varieties adapted to rainfed conditions have been formally released. Participatory approaches, including participatory varietal selection (Witcombe et al. 1996) offer one way to overcome these constraints by involving farmers directly in the process of variety improvement and testing, as well as by utilizing informal seed systems for dissemination. These informal approaches have been adopted by many institutions in both Asia and Africa. PVS aims primarily to accelerate the transfer of new lines to farmers fields and determine the lines which farmers wish to grow with the agronomic and quality traits match the farmers' need, and the magnitude of gender differences. PVS has been reported as an efficient approach for disseminating new improved varieties (Joshi and Witcombe 1996; Ortiz-Ferrara et al. 2001, 2007; Thapa et al. 2009; Witcombe et al. 2003). It is capable of better addressing farmers needs of new varieties that very often are not recognized using conventional non-participatory varietal development approach. PVS could complement ongoing varietal development efforts in the region to help farmers by providing them with a wider option of germplasm to evaluate and adopt under their own conditions (Witcombe et al. 1996, 2003). PVS was successfully used in maize (Zea mays L.) in Africa (De Groote et al. 2002; Snapp 1999) and has proven to be an efficient approach to developing and disseminating new varieties through close collaboration with farmers in South Asia as well (Ortiz-Ferrara et al. 2007). The program reported in this study was implemented by the Dryland Agricultural Research Institute (DARI) in collaboration with the International Center for Agricultural Research in the Dry Areas (ICARDA) and the Centre for Sustainable Development (CENESTA) to initiate a strategy based on breeding for specific adaptation targeted to diversified less favorable environments for increasing crop yield. The objectives were to identify rainfed barley genotypes via PVS in marginal areas of western Iran, and to compare genotypes in both research station and farmer's fields using analytical tools such as the univariate and multivariate methods (biplot methodology) not commonly used by PVS practitioners. Materials and Methods In the cropping season, 68 rainfed barley genotypes selected from the joint Iran/ICARDA barley program and two checks (one local and one improved) were grown in two farmers fields (Zaman Abad and Nojoub) and one research station (Sararood station, one of the main stations of the Dryland Agricultural Research Institute (DARI) in Kermanshah province, Iran). One genotype which was missing in one of the locations was omitted from the analysis. Twenty-three farmers from Zaman Abad village and 16 farmers from Nojoub village participated in the evaluation of genotypes. The participants were mainly small-scale farmers with 2-5 hectares of land in marginal areas. In each of these three sites, the experiment was laid out as unreplicated row-column design with five rows and 20 columns, and two checks (Mahali and Sararood-1) each replicated 15 times. Plot size was 3.75 m 2, 2.5 m long, six rows and 25 cm between rows. The research site (N ; E 47 17, 1351 AMSL) is located in the moderately cold region in western Iran that experiences minimum and maximum temperatures of -20 and 45ºC, respectively, and days of freezing temperatures annually. The average long-term annual precipitation is estimated to be 455 mm, consisting of 90% rain and 10% snow. The soil at the site was clay loam. Fertilizer rate was 50 kg N ha 1 and 50 kg P2O5 ha 1 applied at planting. The plots were harvested to measure grain yield per plot which was then converted to kg ha -1. Statistical analysis The farmers and one barley breeder independently carried out their visual selection using a 1-5 scale, where 1 is very poor, 2 is poor, 3 fair, 4 good, and 5 very good. Farmers selection was done in both villages while the breeder did his visual selection only in Zaman Abad. The original ranks of the farmers and breeder were subjected to univariate and multivariate analyses. Adjustment was performed on yield data as follows: where is the mean yield of replicated genotypes in block i and is the overall mean yield of the replicated genotypes. To investigate the consistency of the farmers' ranks on each genotype, variance (S 2 ) for each genotype was calculated (Huehn 1979; Nassar and Huehn 1987). The stability of the farmers' ranks was also measured by the combined use of the coefficient of variation (CV) and mean farmers' ranks (Francis and Kannenberg 1978). Genotypes with low CV and high mean farmers' ranks were regarded as the most desirable. The strati-

3 JCSB 2011 (December) 14 (4) : 281 ~ fied ranking technique of Fox et al. (1990) consists of scoring the number of farmers for who each genotype ranked in the top (scales 4 and 5), middle (scale 3), and bottom (scales 1 and 2) thirds of the tested genotypes. The percentages of farmers for which a genotype ranked in the top third, middle third, and lowest third are the TOP, MID, and LOW values, respectively. A genotype that occurred mostly in the top third (high TOP value) was considered as a genotype preferred by the majority of the farmers. GGE biplot analysis was performed using farmers' ranks at each village and both farmers' ranks and grain yield at both research and farmers fields, to identify superior genotypes. GGE biplot analysis is a method of graphical analysis of multienvironment data that can be used both for quantitative and qualitative measurements. It is different from regular biplot that simultaneously displays both genotypes and environments or traits (Gabriel 1971). The GGE biplot is a graph that displays the main genotype effect (G) and the genotype-by-environment (GE) interaction in a multi-environment trial (DeLacy et al. 1996; Yan and Kang 2003). It is constructed by plotting the first two principal components (PC1 and PC2, also referred to as primary and secondary effects, respectively) derived from singular value decomposition (SVD) of the environment-centered data. In this study, the ranks matrix was used for biplot analysis. Within the biplot, genotypes were plotted as points and farmers' ranks as vectors. The biplot was used to further assess the patterns of the relationships among genotypes, among farmers' ranks, and among both genotypes and farmers' ranks. Genotypes close to the origin are average in their performance according to farmers' ranks. The lines that connect the biplot origin and the markers for the farmers' ranks are the farmers' ranks vectors. The cosine of the angle between the vectors of farmers' ranks approximates the correlation coefficient between them (Kroonenberg 1995; Yan 2002). A small angle between two vectors indicates a strong positive correlation; right angle indicates zero correlation, and obtuse angle indicates a negative correlation. The GGEbiplot software (Yan 2001) was used to conduct the analyses. Results Data description The mean grain yields ranged from 4,248 kg ha -1 in the research station, to 2,620 kg ha -1 in Nojoub, and to 2,582 kg ha -1 in Zaman Abad. Nearly 59% of the variation of the environmentally standardized data was due to the genotypic effects and 41% to GE effects. The GE interaction was mostly associated with Zaman Abad which had a nearly zero correlation with the research station and a weak positive correlation with the other village (Fig. 1). In Zaman Abad, the adjusted mean yield ranged from 997 kg ha -1 (genotype G44) to 4,386 kg ha -1 (genotype G12) (Table 1). The five top-yielding genotypes were G12 followed by G3, G62, G63, and G34 (with > 4,000 kg ha -1 ). The ranks for these five genotypes by the breeder were 5, 4, 3, 5, and Fig. 1. Biplot of grain yields in one research station (ST) and two villages (Nojoub) and Zaman Abad. The numbers 1-69 are the genotype codes; the numbers 68 and 69 are the local (Mahali) and improved (Sararood-1) barley cultivars, respectively. 3, respectively, while the mean ranks by farmers were 3.0, 3.9, 3.7, 3.8, and 3.3, respectively. The correlation coefficient between farmers and breeder ranks was 0.16 (P > 0.05). The mean of farmers' ranks varied from 2.1 to 4.2. The top yielding genotypes received from average to high mean farmers' ranks. Genotypes G57, G54, G18, G24, and G56 received the highest mean farmers' ranks of 4.2, 4.2, 4.1, 4.0, and 4.0, respectively. These genotypes had low to high grain yield (Table 1). A total of 31 genotypes numerically outyielded the local check (G68, with grain yield 2,554 kg ha -1 ) and 23 genotypes outyielded the improved variety check (G69, with grain yield 2,837 kg ha -1 ). The variance of the farmers' ranks ranged from 0.17 (G18) to 1.50 (G55) (Table 1). Genotypes with the lowest variances were G18, G69, G39, G68, and G30, indicating that farmers strongly agreed to either select or reject these genotypes. The mean farmers' ranks for G18 was 4.1, showing that this is an ideal genotype combining the highest mean rank with the lowest variance. The highest variances were observed for the genotypes G55 followed by G59, G49, G11, and G48, indicating that there was a strong disagreement among farmers in the ranking of these genotypes. To further describe farmers' ranks, the coefficient of variation (CV) was also calculated. The CV values ranged from 10.2 (G18) to 42.3 (G51). The genotypes G18 followed by G60, G15, G45, and G39 had the lowest CV value, while G51, G55, G59, G9, and G46 had the highest values. The genotypes with the lowest CV values had higher mean ranks, i.e. 4.1, 3.9, 3.9, 3.7, and 2.9, respectively, while the genotypes with the highest CV values had low to average mean ranks, i.e. 2.3, 3.0, 3.3, 2.4, and 2.6, respectively (Table 1). The TOP values ranged from 0%, corresponding to G7, to 100%, corresponding to G18. Based on the TOP values, G18, G56, G54, G60, G63, G15, G57, G24, G40, and G67 were the genotypes highly preferred by farmers because they were ranked in the top third of the genotypes by a high percentage of farmers. The improved (G69) and the local (G68) checks had a TOP value of 8 and 4%, respectively; and 91 to 97% of the

4 284 Identifying Superior Rainfed Barley Genotypes Using PVS Table 1. Yield performance (YLD), breeder's rank (BR), mean of farmers' ranks (MFR), variance (S 2 ), CV of ranks, and the parameters of Fox et al. (1990) for 69 barley genotypes tested in the Zaman Abad and Nojoub villages Zaman Abad village Nojoub village Code* YLD YLD BR (Kg ha -1 ) # MFR S 2 CV TOP MID LOW MFR S CV (Kg ha -1 ) 2 TOP MID LOW G1 2, , G2 1, , G3 4, , G4 2, , G5 2, , G6 2, , G7 3, , G8 2, , G9 2, , G10 3, , G11 2, , G12 4, , G13 3, , G14 2, , G15 1, , G16 2, , G17 2, , G18 3, , G19 2, , G20 2, , G21 1, , G22 2, , G23 1, , G24 2, , G25 3, , G26 2, , G27 2, , G28 2, G29 3, , G30 2, , G31 3, , G32 1, , G33 1, , G34 4, , G35 2, , G36 1, , G37 1, , G38 1, , G39 2, , G40 1, , G41 1, , G42 2, , G43 2, , G , G45 2, , G46 1, , G47 2, , G48 3, , G49 2, , G50 2, , G51 2, , G52 1, , G53 2, , G54 2, , G55 3, , G56 1, , G57 3, , G58 2, , G59 3, , G60 3, , G61 2, , G62 4, , G63 4, , G64 2, , G65 3, , G66 2, , G67 2, , G68 2, , G69 2, , *Genotype codes: G1-G67 are the barley breeding lines selected from Iran/ICARDA joint project; and the G68 and G69 are the local (Mahali) and improved (Sararood-1) barley cultivars, respectively. # BR: Breeder's rank; MFR: Mean of farmers' ranks; S 2 : common variance; CV: coefficient of variation; TOP, MID, and LOW are the parameters of Fox et al. (1990).

5 JCSB 2011 (December) 14 (4) : 281 ~ Fig. 2. Biplot of ranks of 69 barley genotypes by 23 farmers and one breeder in Zaman Abad showing relationship among farmers visual selections. F1-F23 are vectors for farmers ranks and G1-G69 are genotype codes. The numbers 1-69 are the genotype codes; the numbers 68 and 69 are the local (Mahali) and improved (Sararood-1) barley cultivars, respectively. genotypes with similar or higher grain yields had a higher TOP value. The TOP value > 70% indicates that these genotypes were ranked 4 or 5 by more than 70% of farmers (Table 1). The most undesirable genotypes identified by this method were G7 (TOP = 0% and LOW = 58%), followed by G68 (TOP = 4% and LOW= 79%), G69 (TOP = 8% and LOW = 33%), G53 (TOP = 8% and LOW = 25%), and G51 (TOP = 13% and LOW = 58%). The genotypes classified more often by farmers as average were G13, G39 (MID = 71%), G53, G14, G30, G36 (MID = 67%), G2, G10, G19, G28, G33, G43, G44, and G69 (MID = 58%). The genotypes with the highest value of TOP are also the lowest in either MID or LOW or in both of them (Table 1). Therefore, TOP is a useful estimated of farmers preferences (Flores et al. 1998; Fox et al. 1990; Mohammadi et al. 2008). Similar analyses were conducted on the same genotypes tested in Nojoub village (Table 1). In this village, adjusted mean yields varied from 973 kg ha -1 (G28) to 5,640 kg ha -1 (G50). Sararood-1 and the local cultivar yielded 3,768 and 2,379 kg ha - 1, respectively. Amongst the 11 genotypes which yielded more than the best check, G50, G53, G26, G12, G18, and G60 were the top yielding ones with a yield > 4,000 kg ha -1. The mean of farmers' ranks varied from 1.5 (G8) to 4.6 (G54), a wider range than observed in Zaman Abad (2.1 to 4.2), indicating that the Nojoub's farmers discriminated the genotypes more. Eight of the 11 genotypes outyielding the best check also had similar or higher average farmers rank. The top mean ranks were given to G54 followed by G63, G58, G64, and G66. The lowest yielding genotypes were G28, G30, G57, G1, G20, G36, and G9 (with yield kg ha -1 < 1,500). The mean ranks of these genotypes were average and ranged from 2.38 to 3.0. Variances obtained from farmers ranks ranged from 0.13 (G37) to 1.05 (G55) indicating that variation among the ranks of G55 was about 10 times more than the variation among the ranks for G37. The G37 followed by G14, G68, G36, G46, G4, and G15 had the lowest fluctuation in ranking by farmers. These genotypes had also low mean ranks which varied from 2.08 to 3.0. The CV values ranged from 12.2 (G37) to 54.4 (G8). The highest CV were observed for the genotype G8 followed by G7, G43, G3, and G49 which had low mean ranks. The lowest CV was found for G63 followed by G36, G46, G14, and G37: these genotypes had mean ranks of 4.3, 2.94, 2.94, 2.81, and 3.0, respectively. The genotypes most preferred by farmers in Nojoub were G63 followed by G54, G64, and G66, although they were average to poor yielders because they were scored in the top third of genotypes by a high percentage of farmers (TOP value > 68%), while the genotypes G4, G7, G9, G14, G16, G28, G29, G50, and the local (G68) were never included in the most desirable group (TOP value = 0%). Sararood-1 had a TOP value of 38% while 7 of the 11 genotypes which outyielded Sararood-1 had TOP values between 50 and 100%. The genotypes G68, G8, G7, G50, G28, and G3 were scored in the low third of genotypes by a high percentage of farmers (LOW > 50%) and they were the most undesirable genotypes. According to the visual selections by farmers, the genotypes most preferred (genotypes with rank = 3) were G37, G14, G36, G46, G47, G11, G15, G16, G52, and G67 (MID value > 75%). Graphical analysis of genotype by farmer interaction In the graphical biplot analysis on the original ranks from the Zaman Abad location, the first two principal components (PC1 and PC2) derived by subjecting the farmers' ranks to singular value decomposition (SVD) accounted for 42.8% of the genotype and genotype-by-farmers' ranks (G + GF) variation (Fig. 2). The maximum angle among most of the farmers is below 90º, suggesting that most of the farmers tend to discriminate genotypes in a similar fashion (Fig. 2) even though the scores given by some farmers, such as farmers 4, 9, 20, and 21 on one, and farmers 1, 2, and 7 on the other side, were almost independent. The vector corresponding to the breeder score was very short indicating that the breeder was less able than most farmers to discriminate among genotypes, at least in the conditions of the farmers field in Zaman Abad. According to Fig. 2, most farmers in Zaman Abad tended to select the genotypes G57, G56, G24, G15, G67, G25, G29, G58, G22, G54, G8, G58, G3, G45, G62, G62, G20, G4, G16, G64, G63, G52, G34, G48, and G49. The remaining genotypes were considered undesirable. The local (G68) and improved variety (G69) checks were ranked low by the farmers. This matches with their mean ranks of 2.1 and 2.5, respectively. As mentioned earlier, the TOP values for G68 and G69 were 4 and 8%, respectively, showing that when compared with new breeding material they were not appreciated by the majority of the farmers. In the case of Nojoub, the first two principal components were significant and accounted for 53.6% (45.1% and 8.5% by PC1 and PC2, respectively) of (G + GF) variation (Fig. 3). Like in Zaman Abad, the angle among most of the farmers' vectors was acute, showing that the majority of the farmers selected a common set of genotypes, namely G63, G54, G64, G40, G58, G18, G12, G32, G61, G5, G65, G66, G26, G35, G62, G6, G56, G25, G55G59, G60, G25, and G25. These genotypes received a

6 286 Identifying Superior Rainfed Barley Genotypes Using PVS Fig. 3. Biplot of ranks of 69 barley genotypes by 16 farmers in Nojoub showing relationship among farmers visual selections. F1-F16 are vectors for farmers ranks and the numbers 1-69 are the genotype codes; the numbers 68 and 69 are the local (Mahali) and improved (Sararood-1) barley cultivars, respectively. higher average score than the checks. One farmer (farmer 8) did not discriminate as well as the majority of the other farmers, and actually ranked highly genotypes which were ranked low by the majority of the other farmers. To investigate the relationship among grain yield in farmers fields and in the research station and their relation with mean of framers' ranks, a biplot analysis was performed (Fig. 4). The biplot in Fig. 4 explained 65.0% of the total variation of the standardized data. The most prominent relation in Fig. 4 is the weak association between grain yields and the average farmers ranks in Nojoub and the absence of association between yields and average farmers ranks in Zaman Abad. The biplot also shows with circles the 12 genotypes which were eventually selected considering their overall performance. The selected genotypes included five, four, and two of the top yielding genotypes in Zaman Abad, Nojoub, and the research station, respectively. They also included four, five, and three of the top ten genotypes for the farmers' rank in Zaman Abad and Nojoub and for the breeder s rank in Zaman Abad, respectively. Discussion Fig. 4. Biplot of yield (GY) and farmers ranks (MS) of 69 barley genotypes at Zaman Abad, (ZA), Nojoub (NO), and Sararood research stations (ST) showing in circles the genotypes eventually selected for the next cycle of selection. The numbers 1-69 are the genotype codes; the numbers 68 and 69 are the local (Mahali) and improved (Sararood-1) barley cultivars, respectively. PVS has been greatly successful in many crops and countries when used in marginal areas with low-resource farmers. It is also effective in more productive environments where it contributed to increased on-farm varietal diversity and faster varietal replacement. However, for productive environments, on-station trials can represent quite well the situation in farmers' fields so the advantages of PVS in favorable environments, while they can still be substantial, tend to be less than those for marginal areas (Witcombe et al. 2003). In Iran, small farmers with less than 5.0 ha of land constitute almost 73% of the country s agricultural producers. In 2006, only 5% of farmers owned more than 20 ha of land. The Dryland Agricultural Research Institute (DARI) is responsible for agricultural development in dry lands and low input farming systems of Iran. The rainfed cereal breeding program is one of the main research programs in DARI which is mainly focused on the improvement of yield potential under rainfed conditions, following a wide adaptation strategy that tended to neglect areas with lower potential for crop production. In this program, varieties were mostly selected under highinput conditions of the research stations and then introduced with technological packages to farmers in the target environments (Haghparast et al. 2009). PVS has successfully identified the genotypes preferred by farmers. Institutionally, these participatory approaches can be used either in parallel to formal testing and release procedures, serving to identify cultivars likely to be adopted by farmers as well as putting seed into the informal sector. Our results showed that genotypic mean yield in the research station was as high as 4,248 kg ha -1, while in Zaman Abad and Nojoub it was 2,584 and 2,735 kg ha -1, a yield reduction of up to 45%. This is a clear example of the differences between experimental yield (on-station condition) and actual yield (condition of farmers' fields). The mean yields of the genotypes in farmers fields were nearly the same as the mean yield of the checks (the mean yield of the checks in Zaman Abad and Nojoub were 2,695 and 3,074 kg ha - 1, respectively). The most obvious reason for the lower yield in the farmers fields is that soil fertility and agronomic management in research stations were different from the farmers growing conditions. In this study, the checks "Mahali" and "Sararood-1" that are commonly grown by farmers in western Iran, were outyielded by many improved lines tested. Even though the difference was not significant, the breeding lines that outyielded the best check received a better score by the farmers indicating that grain yield is not the main or the only selection criterion used by farmers. This also showed that the national barley breeding program has a good understanding of the barley cultivars preferred by farmers. However, not all genotypes showed higher grain yield than the checks indicating that not all the advanced breeding lines

7 JCSB 2011 (December) 14 (4) : 281 ~ selected for high yield on the research station necessarily perform well in farmers field, underlying the importance of decentralized selection (Ceccarelli and Grando 2007). Significant breeding progress and yield gains are evident improved barley lines were compared with the checks in farmers' fields. If the strategy of the breeding program is to improve yield in marginal areas, it has to focus on local adaptation to increase gains from selection and the selection should be exercised directly in the target environment (Atlin et al. 2000; Ceccarelli 1994; Hohls 2001). In this study, the 12 genotypes eventually selected included only 2 of the 10 highest yielding genotypes on the station; additional evidence of the importance of decentralized selection is that only two of the top 10 genotypes for farmers ranks in Zaman Abad were in common with the top 10 genotypes for farmers ranks in Nojoub indicating that farmers' selection criteria may vary from environment to environment and even from farmer to farmer in same environment (Cleveland et al. 1999; Danial et al. 2007); therefore, selection should be based on yield in a range of conditions from favorable to unfavorable environments if the breeder wants to serve a wide range of farmers. Farmers participation represents an added value to the benefits of decentralized selection. In this study, it was shown by the selection done by the breeder in Zaman Abad which was not correlated with that of the farmers. The fact that this is due to the comparison of the decision of an individual with that of a group of individuals is only adding strength to the argument that farmers participation is beneficial because it allows the identification of genotypes which are the most desirable to a community rather to an individual. The importance and the efficiency of farmers' participation were evident also from the positive correlations observed at all locations between yield and farmers' visual selections. A further indication of the efficiency of farmers' selection is given by the high frequencies of farmers' selections which are in the top ranking of genotypes in farmers' fields. Moreover, the efficiency of farmers' selection was shown by the positive correlation coefficients between farmers' scores and grain yield of genotypes in both the station and farmers' fields. The same genotypes tested in different locations showed differential performance indicating the importance of G x E interaction. For example, G18, G56, G54, G60, G63, G15, G57, G24, G40, and G67 were the most preferred genotypes in Zaman Abad, while the most preferred genotypes in Nojoub village were G54, G64, and G66. This shows that PPB must be expanded in more locations with the same set of varieties in order to allow farmers to judge the consistency of performance of the genotypes. Based on genotype ranking through biplot analysis, positive associations among farmers' selection scores was observed in both villages indicating that the selection criteria for most of the farmers were similar. The biplot approach used in this study could help breeders to make better decision about what genotypes should be promoted or released. The combined assessment of performance and its stability is an important advantage, and adds confidence in the decision to promote superior genotypes to a given environment as per the farmers preferences. In particular, PVS research where more qualitative than quantitative data are collected, use of the GGE biplot tool could go a long way in making decision on technology selection and dissemination including improved cultivar (Thapa et al. 2009). Farmers are more dynamic and understand their farming system situation better than anybody else, due to their rich practical knowledge and experience. They have over the years selected and tested varieties based on multiple criteria. The farmers were competent in identifying high yielding genotypes with desirable traits for their environments. The study indicates the importance of PVS in increasing and stabilizing productivity in marginal environments as each specific environment needs to be occupied by the best genotypes. However, active participation of farmers is essential to increase the adoption of improved cultivars in marginal areas particularly in crops of minor importance at the global level. 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