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Effect of Planting Date and Cultivar Maturity in Edamame Quality and Harvest Window

Edamame is a food-grade soybean [Glycine max (L.) Merr.] that is harvested immature between the R6 and R7 reproductive stages. To be labeled as a premium product, the edamame market demands large pod size and intense green color. A staggered harvest season is critical for the commercial industry to post-harvest process the crop in a timely manner. Currently, there is little information to assist in predicting the optimum time to harvest edamame when the pods are at their collective largest size and greenest color. The objectives of this study were to assess the impact of cultivar, planting date, and harvest date on edamame color, pod weight, and a newly minted Edamame Harvest Quality Index combining both aforementioned factors. And to predict edamame harvest quality based on phenological stages, thermal units, and planting dates. We observed that pod color and weight depended on the cultivar, planting date, and harvest date combination. Our results also indicated that edamame quality is increased with delayed planting dates and that quality was dependent on harvest date with a quadratic negative response to delaying harvest. Maximum quality depended on cultivar and planting and harvest dates, but it remained stable for an interval of 18–27 days around the peak. Finally, we observed that the number of days between R1 and harvest was consistently identified as a key factor driving edamame quality by both stepwise regression and neural network analysis. These research results will help define a planting and harvest strategy for edamame production in Arkansas and the United States Mid-South.

INTRODUCTION
Edamame (vegetable soybean) is a food-grade soybean [Glycine max (L.) Merr.], which is harvested immature between the reproductive stages of R6 and R7, when the beans fill 80-90% of the pod (Konovsky et al., 1994;Shanmugasundaram and Yan, 2004). As a vegetable product, the appearance of the pod and bean must be acceptable for end consumers. The main physical attributes of edamame include large seed weight (>30 g per 100 seeds) and large and green crescent shaped pods with two or three seeds (Mentreddy et al., 2002;Shanmugasundaram and Yan, 2004). Production of edamame in the United States is thought to have started in the 1950s including home gardens and food processors. Demand for edamame in the United States has seen a dramatic increase since the early 2000s (Mentreddy et al., 2002). Nuss (2013) reported that between 22,600 and 27,000 Mg of edamame per year was consumed in the United States, estimated to be a $175-$200 million market. The United States is one of the top soybean-producing countries; therefore, soybean growers have the potential to produce edamame competitively, since commodity soybean and edamame share requirements of photoperiod sensitivity, fertilization practices, disease management, and irrigation techniques (Nuss, 2013;Ross, 2013;Ogles et al., 2016).
Soybean development and maturation are divided into vegetative and reproductive physiological stages (Fehr et al., 1971). Reproductive stages are characterized by blooming (R1 and R2), pod development (R3 and R4), seed filling (R5 and R6), and plant maturity (R7 and R8) stages (Fehr et al., 1971). Pod and seed appearance and seed composition change during these reproductive stages. Previous work from our research team, working on food-grade soybean cultivars including edamame materials, demonstrated that protein initially decreases for 3-5 weeks after flowering but then begins to accumulate, contrarily to oil that is accumulated steadily during the early reproductive stages (Saldivar et al., 2011). Also, starch and sucrose contents steadily decrease with seed development, while oligosaccharides remain low in seed until 3 weeks prior to R8 stage (Saldivar et al., 2011). In addition, Xu et al. (2016), working on two edamame cultivars, reported that seed weight peaks at the R6 stage, observed a continuous decrease in seed green color from R5 to R8 stage, and confirmed the report by Saldivar et al. (2011) on seed protein, oil, and carbohydrate accumulation patterns. Such drastic changes in soybean seed composition with stage of development highlight the importance of a timely harvest of edamame to ensure both maximum seed size and an optimal seed composition.
Edamame under commercial production is typically harvested using a modified green bean picker. To spread out crop risks and to even the flow of materials entering post-harvest processing facilities, the edamame crop is typically stagger-planted through various dates and maturity-group combinations. Nolen et al. (2016) reported that techniques such as these can extend the harvesting season to several months and that a staggered harvest is critical due to the short window a cultivar will have acceptable pod size and color. It has been reported that the range from reproductive stages R5.8-R7.0 can be 18-20 days (Purcell et al., 2014); however, Nolen et al. (2016) suggested that the harvest window for an acceptable edamame product can be less than 18 days.
Soybeans will mature faster as the nights become longer (Garner and Allard, 1920). Garner and Allard (1920) added that photoperiodism is a major factor in soybean yield. Johnson et al. (1960) indicated photoperiodism can affect later stages of reproductive development, not just triggering flowering. In addition, some soybean cultivars are less sensitive than others to delayed planting and changes in photoperiod (Johnson et al., 1960), whereas very early cultivars [maturity group (MG) 00 and 0] have been reported not to be sensitive (Polson, 1972). In addition, as the relative maturity increases, the soybean reproductive growth stages become increasingly more sensitive to long nights (Johnson et al., 1960;Major et al., 1975b).
The ability to predict the harvest date of many horticulture crops is based on accumulated thermal units (Tu) above a cropspecific base temperature throughout the crop's growing season (Oliver and Annandale, 1998;Miller et al., 2001). The base temperature below which growth and development of soybean stop is 7 • C (Boote et al., 1998). Previous research has suggested that it is possible to use temperature in correlation with growth (Major et al., 1975b), but it has also been reported that predicting soybean growth stages using thermal units may be no more accurate than using calendar days (Major et al., 1975a). Therefore, the objectives of this study were to first assess the impact of planting date and harvest date on edamame pod color and pod weight on three edamame cultivars of contrasting maturity and growth habit; second, to identify the effect of planting date and harvest date on a newly defined Edamame Harvest Quality Index (EHQI) for each of the three aforementioned cultivars; and, third, to predict edamame harvest quality based on phenological stages, thermal units, and planting dates using Stepwise Regression and Artificial Neural Network Analysis.

Field Experimental Design
The experiment was designed as a split-split plot with three replications. The whole plot was planting date (three levels), the split-plot was edamame cultivar ("8080, " "R08-4002, " and "R09-345"), and the split-split plot was harvest date (eight levels) nested within planting date by cultivar. Harvest was initiated when a cultivar within a planted date reached R5.8 stage (Fehr et al., 1971) on the plot assigned for the first Harvest Date and continued approximately every 5 days on each of the subsequent Harvest Date plots. Harvesting was discontinued when the crop reached R7 (yellowing of pods); therefore, not all harvest date plots were used for every cultivar and planting date, as the total number of harvests depended on speed of crop progression to R7 stage. It is noteworthy that because the three cultivars in this study represent different MGs and because of the variation in photoperiod and temperature across planting dates, a given cultivar could be at different physiological stages across planting dates even if harvested the same number of days after R5.8.
Of the cultivars used, 8080 was an indeterminate MG3 cultivar, whereas R08-4002 and R09-345 were determinate MG5 and MG6 breeding cultivars, respectively. The experiment was grown over 2 years (2014 and 2015) in two locations, the Arkansas Agricultural Research and Extension Center (AAREC) in Fayetteville, AR, United States and the Vegetable Research Station in Kibler, AR, United States. Soils of the former are silt loam (Johnsburg Series; fine-silty, mixed, active, mesic Aquic Fragiudults), while for the latter were very fine sandy loam (Roxana Series; coarse-silty, mixed, superactive, nonacid, thermic Typic Udifluvents) (Soil Survey Staff, 2017). The three planting dates were mid to late May (PD1), mid to late June (PD2), and mid of July (PD3), representing planting dates typically used for edamame production in Arkansas. Each plot consisted of four rows 10.7 m long and 0.91 m wide. The seeding rate was 33 seeds per meter row, resulting in FIGURE 1 | Edamame Harvest Quality Index (EHQI), Hue, and Hundred-Pod Weight (HPW) of three soybean cultivars when planted in three planting dates and subjected to weekly harvest dates, beginning at R5.8 stage and discontinuing at R7 physiological stage.
approximately 16 seeds per meter row at emergence. The plots were managed using standard agricultural practices for irrigation, fertilizer, and pesticides. At harvest, a total of 100 pods were randomly picked by hand throughout the canopy within the middle two rows of each four-row plot. The pods were immediately sealed in plastic bags and placed on ice. Then, the pod samples were blanched in a 100 • C water bath (Mozzoni et al., 2009) and stored in a refrigerator at 1.6 • C to maintain freshness until color determination (Tsay and Sheu, 1991). HarvestDate_DOY: Day of year when harvest occurred. #Days R1-Harvest: Number of days elapsed between R1 and harvest for a given plot. #Days Ve-Harvest: Number of days elapsed between Ve and harvest for a given plot. GDD Ve-R1 (Growing Degree Days to R1): The Tu was calculated as described by Miller et al. (2001) with a base temperature suggested by Boote et al. (1998) for the days between Ve and R1, with data generated by weather station nearby the trial location. GDD Ve-Harvest: Calculated as described by Miller et al. (2001) using observed thermal units between Ve and Harvest. GDD R1-Harvest: Calculated as described by Miller et al. (2001)  Hue: Describes how close a color is to pure red, yellow, green, or blue (values of 0 • , 90 • , 180 • , or 270 • , respectively). Pod hue was calculated, as reported by Rayaprolu et al. (2015), using the equation: Hue = arc Tan ( a b ) HPW (Hundred-pod weight): Weight in grams of 100 pods prior to blanching. EHQI: An index was developed to represent into a single trait the maximization values for IGC, HUE, and HPW. With this index, the greater the value, the greater the quality of the edamame pods. EHQI was calculated as:

Traits Assessed
where HPWmax † was calculated as the maximum hundred pod weight for a given cultivar, planting date, and location and year combination. This index was built considering two key factors in edamame quality, namely, pod color, and pod size. As reported by Wibowo et al. (2020), #1 grade edamame (standard quality) is determined by the number of pods per 500 g (equivalent to our proposed HPW measurement), by the appearance of pods that are not too old and yellow, and by the pod color that must be uniformly green, among other factors. The other parameters in Wibowo et al. (2020) edamame grading system are either under heavy genetic control or under environmental effect but not necessarily affected by the crop developmental stage at harvest time (such as damage by pests and diseases or pod shape). Since Stepwise regression calculated from the dataset of edamame cultivar R08-4002 planted on two Arkansas locations over 2 years in three planting dates and subjected to up to eight harvests dates, approximately 5 days apart, between R5.8 and R7. PlantingDate_DOY was not significant in the stepwise analysis and was not used to build this response surface. seed size, and concomitantly HPW, is under genotypic control (Xu et al., 2016), in our index, we used a ratio of HPW to the maximum HPW observed for a given cultivar across all its harvest dates. Also, the denominator of EHQI includes a measurement of HUE and IGC as a means to counter the effect of pod size in the index; HUE and IGC are multiplicative and placed in the denominator of the index to highlight the greater importance of green color in overall edamame quality as demonstrated by the inclusion of two elements of color in edamame grading systems (Wibowo et al., 2020). HUE and IGC are highly correlated traits, and even though an alternative option would have been to build EHQI index using only one of the traits and weighed it using a square power, the authors decided to utilize both IGC and HUE in the original building of EHQI.

Statistical Analysis
Each Year and Location combination was aggregated into an "Environment" variable that was considered a random factor in all ANOVA, except when predicting the least-square means to be used for the stepwise and neural network analysis of weather variables, under which Environment had to be assumed a fixed effect.

Objective 1. Impact of Cultivar, Planting Date, and Harvest Date on Edamame Pod Color and Pod Weight
The PROC GLIMMIX procedure of SAS 9.4 software (SAS Institute, Cary, NC, United States) was used to analyze HPW, IGC, and Hue, with a model with the following fixed effects. Planting Date was the whole plot, Cultivar was the splitplot, and Harvest Date nested within Planting Date and Cultivar was the split-split plot. The random effects were Environment, Block nested within Environment, and Planting Date by Block nested within Environment. A beta distribution with logit link was used for IGC analysis, whereas a normal distribution with identity link was used for the models of HPW and Hue. Stepwise regression calculated from dataset of edamame cultivar R09-345 planted on two Arkansas locations over 2 years in three planting dates and subjected to up to eight harvest dates, approximately 5 days apart, between R5.8 and R7.

Objective 2. Impact of Planting and Harvest Dates on EHQI of Three Soybean Cultivars
Since each cultivar is expected to have its own HPWmax because of the genetic control of seed size (Xu et al., 2016), ANOVA for EHQI was conducted by cultivar using a model in PROC GLIMMIX of SAS 9.4 with a beta distribution with logit link. For this analysis, Planting Date was whole plot and Harvest Date nested within Planting Date was split plot. The random effects were Environment, Block nested within Environment, and Planting Date by Block nested within Environment.
In addition, to characterize the change in edamame quality over time, regression analysis was conducted for EHQI as response of PlantingDate_DOY, HarvestDate_DOY, and their squared values. A stepwise regression was conducted independently for each cultivar using PROC REG in SAS 9.4, with significance level of 0.15 to enter or remove variables from the linear model and minimum Akaike information criterion (AIC) selection criteria. Modeled parameter estimates were then used to build response surfaces to predict EHQI for all days within the planting and harvesting day-of-year used. Finally, because of a risk of collinearity and/or non-linear responses to some of the variables entering the stepwise procedure, a neural network analysis was conducted using JMP 15.1, executing a random holdback validation and testing multiple different hidden layer structures of TanH, Linear, or Gaussian activation types and two, three, or 10 first and secondary layers. Absolute penalty was implemented, as it was assumed that a few of the variables contribute more than others to the predictive model. The number of tours was set to 1,000 and random seed to 0.5. Factors included in the analysis were PlantingDate_DOY, VeDate_DOY, R1Date_DOY, HarvestDate_DOY, #Days Ve-R1, GDD Ve-R1, #Days R1-Harvest, GDD R1-Harvest, #Days Ve-Harvest, and GDD Ve-Harvest, and the response variable was EHQI.

Impact of Cultivar, Planting Date, and Harvest Date on Edamame Pod Color and Pod Weight
A split-split-plot analysis for edamame HPW indicated a non-significant Planting Date effect (0.2235) or Planting Date by Cultivar interaction (p = 0.2040) but significant Cultivar (p < 0.0001) and Harvest-Date-by-Planting-Date-by-Cultivar interactions (p < 0.0001). Similarly, for Hue and IGC, the Harvest-Date-by-Planting-Date-by-Cultivar interactions were highly significant (p < 0.0001). Those models also indicated significant main effects of planting date (p < 0.0001 and p = 0.0003 for Hue and IGC, respectively) and Cultivar (p = 0.0036 and p < 0.0001 for Hue and IGC, respectively) and a highly significant interaction with Planting Date-by-Cultivar (p < 0.0001 for Hue and p < 0.0001 for IGC). All these results indicated that the responses of HPW, Hue, and IGC must be explored independently by planting date, harvest date, and cultivar combination (Figure 1). Supplementary Tables 1-3 present the least square mean estimates, standard error, and conservative T-grouping for HPW, Hue, and IGC, respectively. In general, it was observed that HPW increased over the first four harvest dates. The earliest-maturity cultivar (8080) presented a significant decrease in HPW for the second and third planting dates when harvesting extended past Harvest Date 6; such drop in HPW was not observed for the later maturity cultivars (Figure 1  and Supplementary Table 1). On the other hand, Hue (Figure 1 and Supplementary Table 2) and IGC (Supplementary Table 3) showed a decrease with soybean physiological development, and the maximum values were observed at R5.8, corresponding to the first Harvest Date. It is noteworthy that Hue and IGC are highly correlated traits (r = 0.99, p < 0.001), and they behaved similarly for all cultivars and planting dates. Future research efforts in edamame may focus on just IGC instead of measuring both traits because IGC is easier to interpret since the objective is to maximize IGC for a dark-green edamame pod product. Finally, Planting Date 2 resulted in the greatest number of weekly harvests possible between R5.8 and R7 for all cultivars, while delayed planting (Planting Date 3) resulted in a rapid reduction in green color (Hue and IGC) and the crop reached R7 faster for all three cultivars compared to the other planting dates (Figure 1).

Impact of Planting and Harvest Dates on Edamame Harvest Quality Index of Thee Soybean Cultivars
ANOVA of EHQI for the three soybean cultivars showed significant effects of Planting Date (p = 0.0003, p < 0.0001, and p = 0.0356 for 8080, R08-4002, and R09-345, respectively) and highly significant Harvest-Date-by-Planting-Date effects (all three cultivars had p < 0.0001). Supplementary Table 4 reports the mean EHQI for the interaction of Harvest-Date-by-Planting-Date. Initial inspection of plots of EHQI over Harvest-Dateby-Planting-Date (Figure 1 and Supplementary Figures 1-3) showed that the third planting date consistently resulted in the greatest EHQI on all three cultivars and that EHQI began to decay over harvest date for all planting dates and cultivars. In addition, EHQI was highly influenced by pod color, whereby EHQI only increased because of an increase in HPW if Hue (or IGC) was at a maximum level. For instance, for Cultivar 8080 and Planting Date 1, it can be observed how EHQI increased on the third and fourth Harvest Date as HPW increased while Hue remained relatively flat (Figure 1). However, when intensity of green pod color decreased, EHQI decreased concomitantly, regardless of a potential increase in HPW. Such situation can be observed for Cultivar 8080 in Planting Date 3, between Harvest Dates 3 and 4, where there was an increase in HPW, a decrease in Hue, and a concomitant decrease in EHQI (Figure 1).
Stepwise regression analysis retained linear and quadratic terms for PlantingDate_DOY and HarvestDate_DOY for cultivars 8080 and R09-345, but the linear PlantingDate_DOY was non-significant for R08-4002. We observed that regression of EHQI using PlantingDate_DOY, HarvestDate_DOY, PlantingDate_DOY 2 , and HarvestDate_DOY 2 fitted the data well, with adjusted R 2 values ranging from 34.7 to 50.7% and very low root mean square error (RMSE) (ranging from 0.028 to 0.059), indicating low standard deviations for the unexplained variance in the models ( Table 1). Regression parameter estimates showed a quadratic increase of edamame quality with delayed planting; on the contrary, we observed a quadratic decrease of quality with delayed harvesting for all cultivars ( Table 1).
For cultivar 8080, our data indicated delayed planting increased EHQI, and that EHQI decreased with delayed harvest; however, near the peak, EHQI remained fairly stable (within 0.024 units EHQI, or one standard error) for 27 days (Figure 2). For cultivar R08-4002, we also observed that delayed planting resulted in greater EHQI and that the quality decreased quadratic with delayed harvest; however, this cultivar did not retain EHQI well with delayed harvest, and EHQI remained within one standard error (0.026 EHQI units) from peak for 18 days (Figure 3). Finally, cultivar R09-345 showed the least total EHQI from all cultivars, yet we still observed a quadratic improvement with delayed planting and a quadratic decrease of EHQI with delayed harvest (Figure 4). R09-345 had a harvest window

Prediction of Edamame Harvest Quality Index Based on Phenological Stages and Thermal Units Using Stepwise Regression and Artificial Neural Network Analysis
A stepwise regression and an Artificial Neural Network Analysis were conducted for EHQI using variables that included days to various phenological stages and thermal units accumulated to key phenological stages. Stepwise regression models found #Days R1-Harvest to be significant across all three cultivars (Table 2), with a negative impact on quality, as the longer period between initiation of flowering and harvest resulted in lower quality. Other significant terms in the model included #Days Ve-R1 for R09-345 and #Days Ve-Harvest for R08-4002. Interestingly enough, the only cultivar that responded to planting date and thermal units was 8080, which is an indeterminate, early cultivar (MG3) and had significant PlantingDate_DOY, VeDate_DOY, HarvestDate_DOY 2 , GDD Ve-R1, and GDD R1-Harvest (Table 2). A Neural Network model with NTanH parameter of 10 nodes resulted in the lowest RMSE for all three cultivars as compared to models with multiple combinations of TanH, Linear, or Gaussian activation types and two, three, or 10 first and secondary layers (data not shown). For cultivar 8080, we observed that #Days R1-Harvest was the most important variable in predicting EHQI, with a total effect of 0.503 that was three times larger than the next variable in total effect. Also, for cultivar 8080, the variables with main effect greater than 0.100 included #Days Ve-Harvest, #Days R1-Harvest, GDD Ve-Harvest, HarvestDate_DOY, and GDD Ve-R1 (Table 3). For soybean cultivar R08-4002, we observed that #Days R1-Harvest also had the largest contribution to the predictive model for EHQI (total effect 0.314). Additionally, for R08-4002, we observed that the variables with main effects greater than 0.100 included #Days R1-Harvest, GDD R1-Harvest, R1Date_DOY, VeDate_DOY, and PlantingDate_DOY (Table 4). Lastly, for the prediction of EHQI of R09-345, we observed that #Days Ve-R1 had the largest total effect (0.201) and that the following variables each had main effects greater than 0.100: HarvestDate_DOY, #Days Ve-Harvest, #Days R1-Harvest, GDD R1-Harvest, and R1Date_DOY (Table 5).

DISCUSSION
High edamame quality is characterized by large pod size and intense and uniform green pod color (Wibowo et al., 2020). Panthee et al. (2004) suggested that seed size has high heritability, indicating that the trait should be controlled more by genetic than environmental variances. A study by Beatty et al. (1982) found that seed weight was not significantly different from April 15 to May 15 planting date but dropped significantly each month from a May 15 to July 15 planting date. Similarly, in our study, we found that HPW depended on the interaction between planting date and harvest date and that HPW increased during the first four Harvest Dates for all cultivars and planting dates.
The second component of edamame quality is intensity of pod green color. Of the cultivars chosen for our study, R09-345 has green seeds at R6 but develops black seed coat color at maturity. Such cultivar showed significantly lower pod Hue and IGC and low overall EHQI than the other two cultivars (Figure 4 and Supplementary Tables 2, 3), suggesting that cultivars whose seed turn black or brown at maturity will not have the same pod color at R6 compared to cultivars that either stay green or turn yellow at the R8 reproductive stage. Thus, cultivars with dark seed coat at maturity may not be appropriate for fresh/frozen market edamame production.
We observed that, in general, delayed planting maximized EHQI. Mean separations for EHQI analyzed as a split-plot design (Supplementary Table 4) showed that the first harvest dates were usually not statistically different from each other, but that as harvest was delayed, EHQI dropped. We also observed a quadratic decrease of EHQI with delayed harvest in all cultivars from our regression analysis. The harvest window seemed planting date and cultivar dependent. The window for maximum EHQI was shorter in late plantings than in earlier planting dates (Supplementary Figures 1-3). Near the cultivar optimum, harvest window for EHQI ranged from 18 to 27 days. The low end of the spectrum agrees with the 18-day window reported by Nolen et al. (2016), but cultivar 8080 showed a much larger window where quality did not drop. Therefore, edamame companies aiming for high quality must procure late planting and ensure logistics are in place for earlier and timely harvests 10-15 days after R5.8 stage is observed. This must be achieved by carefully planning the logistics of field equipment availability, field access under unfavorable weather/road conditions, and processing house turnaround times. On the contrary, early plantings maintain quality for longer periods of time, albeit not maximizing EHQI; therefore, edamame-growing companies could target earlier plantings to marginal grounds, farms with difficult access, or periods where the processing plant is at its peak, all while managing the maturity of the cultivar to spread out flowering (and harvest) timelines.
The early maturing indeterminate cultivar 8080 showed a significant response to thermal units (GDD Ve-R1 and GDD R1-Harvest) in stepwise regression analysis, while the latematurity cultivars did not. This agrees with expected soybean response where the temperature is considered a modifier to the effect of photoperiod whereby short days enhance reproductive development rate (Setiyono et al., 2007) and with the expected insensitivity of earlier maturity to photoperiod , thus enhancing the opportunity for temperature responses.
Both stepwise regression and Neural Network Analysis identified #Days R1-Harvest as a key variable affecting EHQI for all three soybean cultivars. Additionally, when looking at the prediction values for EHQI based on Neural Network and stepwise analyses (Figure 5), we observed that formulas from stepwise analysis tended to overpredict lower and underpredict higher EHQI values, whereas Neural Network prediction was more consistent over the range of data. However, the simplicity of stepwise predictions involving a simple model with few parameters for cultivars of maturities adapted to Arkansas could make field assessments easier. Future research is needed to test these prediction models on other field-grown edamame cultivars to explore their applicability in forecasting harvest decisions.
Finally, and even though our research did not study pod or seed yield, it is a very important criterion for edamame farmers as they need to balance yield and quality of their end product. Therefore, agronomic practices must be used to balance increased seed and pod yield resulting from earlier planting dates (De Bruin and Pedersen, 2008;Mourtzinis et al., 2017) and enhanced seed and pod quality observed from later planting dates.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

AUTHOR CONTRIBUTIONS
DM was responsible for the investigation, methodology, and writing of the original draft. LM was responsible for supervision, data curation, data analysis, and editing and reviewing the final document. MS, MO, LF-P, AA, and CW were responsible for the investigation and editing and reviewing the final document. PC acquired funding and conceptualized the project and edited and reviewed the final document. All authors contributed to the article and approved the submitted version.

ACKNOWLEDGMENTS
The authors thank the Arkansas Soybean Promotion Board for its sponsorship of this project.

SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2020. 585856/full#supplementary-material  Table 1 | Least-square means, Standard Error, and Conservative T-grouping of Hundred Pod Weight (HPW) per cultivar, planting and harvest date combination analyzed on a split-split block design with block and environment as random factors. Levels not connected by same letter are significantly different at α = 0.05.
Supplementary Table 2 | Least-square means, Standard Error, and Conservative T-grouping of Hue per cultivar, planting and harvest date combination analyzed on a split-split block design with block and environment as random factors. Levels not connected by same letter are significantly different at α = 0.05. Table 3 | Least-square means, Standard Error, and Conservative T-grouping of Intensity of Green Color (IGC) per cultivar, planting and harvest date combination analyzed on a split-split block design with block and environment as random factors. Levels not connected by same letter are significantly different at α = 0.05. Table 4 | Least-square mean, Standard Error, and Conservative T-grouping of Edamame Harvest Quality Index (EHQI) by cultivar, per planting and harvest date combination, analyzed as a split block design with block and environment as random factors. Levels not connected by same letter are significantly different at α = 0.05. (A) R08-4002, (B) 8080, and (C) R09-345.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Evaluation of Common Bean ( Phaseolus vulgaris L.) Germplasm for Resistance to Angular Leaf Spot Disease [ Phaeoisariopsis griseola (Sacc.) Ferraris] under Cold Arid Conditions of Ladakh

An experiment was conducted to identify promising accessions suited for cultivation under cold arid conditions of Leh, Ladakh. Twenty two accessions of French bean were screened for resistance to angular leaf spot disease during the spring seasons of 2016 and 2017 in the Experimental field of High Mountain Arid Agriculture Research Institute (HMAARI) Kashmir University of Agricultural Sciences and Technology of Kashmir. The study revealed that under natural epiphytotic conditions none of the accessions was resistant in reaction. Eight accessions viz ., Acc-WB22, Acc-1643, Acc-1690, Acc-811, Acc-4564, WB-1247, WB-185 and Acc-252 were moderately susceptible. All other fourteen accessions viz ., Acc-966, Acc-335, Acc-1129, Acc-21529, Acc-WB216, Acc-719, WB-956, Acc-622, Acc-1492, Acc-195, Anupama, WB-6, WB-257 and Local exhibited susceptible reaction.

INTRODUCTION
Common beans (Phasolus vulgaris L .) also called as Rajmash or Rajma (Hindi) or haricot bean or kidney bean or common bean or snap bean or french bean, occupies premier place among grain legumes in number of countries including India . Legumes constitute the third largest family of higher plants, with 20,000 species and second in agricultural importance based on area and total production (Razvi et al. 2018). Among grain crops, pulses (food legumes) rank third after cereals and oilseeds in terms of total world production. Pulses are rich in proteins and represent an important source of dietary protein for humans and animals. The proteins are generally composed of high amount of lysine, while the amount of methionine and cysteine is less. However, consumption of legumes and cereals results in a balanced diet of energy and protein.
Common bean is an important legume crop in the daily diet of more than 300 million people of the world's population (Hadi et al. 2006;Meziadi et al. 2016). In particular, in developing countries, the importance of common bean is beyond limit as a source of cash and full food nutrients (Popelka et al. 2004;Hadi et al. 2006;Akhavan et al. 2013;Meziadi et al. 2016). Legumes are also an important source of some essential minerals (Grusak, 2002). The legumes have been observed to reduce blood cholesterol levels (Andersen et al., 1984). It is consumed in different forms including the leafy vegetable, pods, green grains and as dry beans (Katungi et al. 2009). It is a source of vitamin-B, calcium, iron, phosphorus and zinc which are essential for human growth, health and development. According to Rusuku et al. (1997), in the developing world, this leguminous crop is produced subsistantly by women farmers' who market approximately 40% of their produce estimated at US $452 million, while the rest of the crop is used for home consumption.
It is largely grown in Himachal Pradesh, Jammu and Kashmir, Uttar Pradesh, North Eastern Hills, Darjeeling, South plateau Hills (Nilgiri and Palni hills) Mahabaleshwar, Ratnagiri (Maharastra) and Chickmanglore (Karnataka) having mild climate with humid environmental conditions (Amit Koul et al. 2018). Although there has been increase in bean production due to expansion into marginal Evaluation of Common Bean (Phaseolus vulgaris L.) Germplasm for Resistance to Angular Leaf Spot Disease... agricultural lands, productivity has not shown any encouraging improvements. Typical bean yields obtained on farmers fields are only 20% to 30% of the genetic potential of improved varieties (W ortman et al.1998). One of the reasons for low productivity is lack of effective disease management practices including lack of disease resistant cultivars. The development of cultivars with improved resistance to biotic and abiotic stresses has long been a primary goal for many bean breeding programs ). Among the diseases Angular leaf spot (ALS) of common bean caused by the imperfect fungus Phaeoisariopsis griseola (Sacc.) Ferraris, is one of the most damaging and widely distributed disease. This disease mainly infects leaves and pods, inducing premature leaf dropping and consequently reduction in grain quality ). Losses in grain yield caused by ALS can reach 80% (Singh and Schwartz, 2010). Use of resistant varieties combined with other disease management practices is regarded as the most practical approach to disease control at the farm level. The greatest setback to this strategy is the high pathogenic variability occurring on Phaeoisariopsis griseola (Sacc.) that renders bean varieties that are resistant in one location or year susceptible in another and also its incidence and severity has increased in the areas of common bean production (Stenglein et al. 2003). It is considered that the use of resistant cultivars is an efficient, safe and in expensive technique accessible for bean growers (Ferreira et al. 2000). In fact, this strategy is the most effective and sustainable method for controlling bean diseases (Oliveira et al. 2008). An important step towards achieving this goal involves testing of the available germplasm to identify potential doners for resistance. Although currently there are no well adapted parents available for breeding, hence to draw genes from both local and conserved material, this study was undertaken to identify potential sources of resistance to angular leaf spot from the accessions maintained and collected from major bean growing regions of the state. Common bean has a great scope in Ladakh as the road remains closed for almost six months during winters and no fresh vegetable is available during that season.

MATERIALS AND METHODS
Twenty two common bean accessions (Table 2) selected from germplasm collection maintained at dry land agriculture research station K. D. Farm, Old Air Field, Rangreth, Srinagar, India were screened against the angular leaf spot disease under natural conditions at High Mountain Arid Agriculture Research Institute Leh, Ladakh SKUAST-K during 2016 and 2017. The experiment was laid out in a complete randomized block design with three replications in each year. Recommended agronomic practices were followed to raise a good crop. From each representative collection, 10 plants were selected randomly, kept unsprayed throughout the season and were tagged for the assessment of the disease. All the leaves of the ten plants were counted and then grouped as healthy and diseased. The disease incidence and intensity was assessed in the month of September. Per cent disease incidence was worked out as per the following formula given by (James,1974): No. of diseased leaves Total no. of leaves examined For assessment of disease intensity, Twenty two accessions of common bean were screened at flowering stage of crop. Observation on angular leaf spot was recorded from flowering till maturity of the crop using Centro International de Agricultura Tropical (CIAT) 1-9 scale adapted from (Inglis et al. 1988) and (Razvi et al. 2017) which is given in Table 1. x 100

RESULTS AND DISCUSSION
The evaluation study of 22 common bean accessions conducted during the year 2008 and 2009 under natural epiphytotic conditions against angular leaf spot (Phaeoisariopsis griseola (sacc.) Ferr. ) indicated that disease occurred in variable proportion on all the tested cultivars during both years (Tables 2 and 3). However, analysis of data showed a differential response among the accessions with regard to incidence as well as intensity.

Disease incidence
The results revealed that disease incidence was significantly different for two years and minimum of 43.61 per cent was Evaluation of Common Bean (Phaseolus vulgaris L.) Germplasm for Resistance to Angular Leaf Spot Disease... as against 23.14 to 66.17 per cent in 2017. The analysis of the pooled data for two years indicated that most of the accessions evaluated were susceptible to the disease but there existed a significant difference in the disease incidence among different accessions. Maximum disease incidence (66.32 %) was recorded in the accession local (local rajma) which was statistically at par with Acc-1492, WB-956, Acc-719, Acc-21529, Acc-1129 and Acc-662 with average incidence of 63. 67, 63.52, 61.80, 60.75, 59.65 and 59.19 respectively. The minimum disease incidence was recorded in genotype WB-22 which was statistically at par with Acc-1643 with average incidence of 21.77 and 23.56 per cent respectively. Rest of the accessions observed have significant differential response to the maximum and minimum disease incidence.

Disease intensity
The disease intensity was significantly different for two years and minimum of 29.73 per cent was recorded in the year 2016 and maximum of 33.16 per cent in 2017. The disease intensity among the accessions ranged between 11.63 to   WB-956, Acc-622, Acc-1492, Acc-195, Anupama, WB-6, WB-257, Local Highly susceptible(HS) 50.1 and above susceptible. The development of ALS resistance genotypes can be expected to increase profitability by reducing the amount of fungicides used to produce a crop. Plant species have a defense mechanism to avoid and resist pathogens and pests. Identification and utilization of common bean resistant sources to P. griseola contributes greatly to management of the disease, since resistant varieties are the most practical and easily adapted strategy. However breeding for resistant varieties has been made difficult by the high pathogenic variability of P. griseola. An effective way of breeding for durable resistance to the highly variable pathogen is by use of minor genes. The advantage of the host resistance is that once the technology has been developed, it is packaged in seed which is easy to disseminate and does not require any additional handling by the farmers, other than the normal crop production practices. Several common bean lines with good levels of resistance to different isolates of P. griseola were identified (Pastor-Corrales et al.1998;Mahuku et al. 2003). Genotypes AND 277, MAR-2, Mexico 54, BAT 332 and Cornell 49249 were identified as potential sources of resistance having dominant genes that govern plant resistance to certain races of P. griseola Sartorato et al. 2000;Aggarwal et al. 2004;Caixeta et al. 2005: Pereira et al. 2015. Genotype Ouro Negro was reported to have dominant gene that controlled resistance to P. griseola races 63-39 and 31-32 (Sanglard et al. 2013). The common bean genotypes G10474 and G1090 were reported to have a single dominant gene conditioning resistance to two P. griseola pathotypes (Mahuku et al. 2004 and2011). Studies by , revealed that common bean conditioning resistance to P. griseola race 31-0. Resistance to ALS disease has been shown to be inherited quantitatively. Oblessuc et al. (2012) showed the existence of seven QTL's that had variable magnitudes of phenotypic effects under different environments. Kimno et al. (2016) reported that only one out of the 34 entries studied showed field resistance to ALS and further revealed that there remains a need to identify further donors of resistance. Hence the moderately susceptible accessions identified during the present investigation (Table 4) can be screened at different stages over locations and years to confirm their reaction to angular leaf spot so that promising accessions/resistant donors can be identified and used in future breeding programs for the development ALS resistant varieties.
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Evaluation of Common Bean (Phaseolus vulgaris L.) Germplasm for Resistance to Angular Leaf Spot Disease...


s2fieldsofstudy:
Agricultural And Food Sciences
Biology

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In-vitro evaluation of botanicals and fungicides against Bipolaris sorokiniana, causing spot blotch of wheat

Spot blotch disease of wheat caused by Bipolaris sorokiniana; is one of the most concerning disease in warm and humid regions of Nepal due to its widespread prevalence and increasing severity. An in-vitro experiment was conducted to evaluate the efficacy of different botanicals and fungicides against Bipolaris sorokiniana, in National Plant Pathology Research Centre’s laboratory at Khumaltar, Lalitpur in 2019. The experiment was done in completely randomized design with three replications by poison food technique. Five botanicals viz. neem, garlic, eucalyptus, bojho and asuro at three different concentrations (5%, 10% and 15%) and eight fungicides viz. sectin, curex, bavistin, vacomil plus, saaf, sajha, criptan and tilt at three concentration levels (25 ppm, 50 ppm and 100 ppm) except curex (50 ppm, 100 ppm and 200 ppm) were used to evaluate the effect on the radial mycelial growth and mycelial growth inhibition percentage of the pathogen. The different botanical extracts and fungicides in different concentrations inhibited the mycelial growth of fungus significantly (p<0.01). The highest mycelial growth inhibition percentage was found by the application of garlic clove extract (52.85%) at 15% which was followed by bojho (52.48%) at 15% concentration. While tilt was found effective fungicide which completely inhibits the mycelial growth at all concentrations. Also, fungicides viz. sajha (79.78%), saaf (73.59%) and sectin (70%), at 100 ppm were found effective in controlling the pathogen as compare to others. Mycelial growth inhibition of the fungus was found to be increased with the increase in concentrations of botanicals and fungicides.

INTRODUCTION
Wheat (Triticum aestivum L.) is third most important cereal crop of Nepal after rice and maize based on both area (703,992 ha) and production (2,005,665 tons) (MoALD, 2018/19). It is grown all over the country; however, plain areas are the main growing regions in Nepal. The lower yield of wheat is due to several biotic and abiotic factors such as disease, poor management practices, soil fertility status etc. (Ruckstuhl, 1997;Regmi et al., 2002). Among the biotic factors, diseases are the most important constraints of wheat production in Nepal. Helminthosporium leaf blight (HLB) also commonly called as foliar blight (FB), a complex of spot blotch (Bipolaris Journal of Agriculture and Natural Resources (2020) 3(2): 296-305 ISSN: 2661-6270 (Print), ISSN: 2661-6289 (Online) DOI: https://doi.org/10. 3126/janr.v3i2.32534 sorokiniana) and tan spot (Pyrenophora tritici repentis); disease of wheat is very severe in Terai areas. High temperature and high relative humidity favour the outbreak of the disease, in particular in South Asia's intensive 'irrigated wheat-rice' production systems (Kumar et al., 2002). However, the leaf blight disease has been increased in areas having moderate temperature and will be a problem in future for wheat growers of Kathmandu valley and similar mid-hills regions of Nepal (Bhandari, 2017). The disease occurs every year in warm wheat growing areas of Nepal in moderate to severe form (Duveiller et al., 2005). The losses due to spot blotch in warm regions of Nepal ranged from 23 -40% depending on the genotypes and other environmental factors (Bhandari and Tripathi, 2005;Sharma and Duveiller, 2006).
A number of systemic fungicides with different modes of action and targets have been developed to reduce the losses caused by the disease (Pasquer et al., 2005). Use of chemical pesticides is the most frequent management practice but not always recommended due to environmental concern and health hazards. So, in search of alternative to chemicals, an ecofriendly and sustainable management option can be the use of different botanicals which have antifungal activity to the spot blotch pathogen. Very few works have done using botanical extracts and fungicides in the country to control spot blotch of wheat. It is important to examine the effect of different concentration of botanical extracts and different fungicides commonly available in market to control this disease. Hence, the study was undertaken to evaluate different botanical extracts and chemical fungicides for their efficacy against B. sorokinana under in vitro condition.

Experimental site and pure culture preparation of Bipolaris sorokiniana
The experiment was conducted in the laboratory of National Plant Pathology Research Centre (NPPRC), Khumaltar, Lalitpur, Nepal. Samples of spot blotch disease were collected from wheat growing fields of Khumaltar during 2019. Diseased parts were cut into 1 cm pieces and sterilized with 1% sodium hypochlorite (NaOCl) for 30 seconds which was then rinsed with distilled water for three times. Those cut pieces were dried under laminar flow and placed on water agar plates and incubated at 25°C for two days. Microscopic observation confirmed the presence of Bipolaris sorokiniana. Pathogen pure culture was performed on PDA plates under aseptic condition by taking single germinating spore in water agar. The plates were incubated at 25°C for 1 week until full growth obtained and pathogen sub culture done aseptically for further study.

Preparation of Botanical extracts
The botanicals used for the experiment were Neem (Azadirachta indica), Garlic (Allium sativum), Eucalyptus (Eucalyptus globolus), Bojho (Acorus calamus) and Asuro (Justicia adhatoda). The cloves of garlic, rhizome of bojho and leaves of neem, eucalyptus and asuro were used to prepare extract. All these five botanical extracts were used in 5%, 10% and 15% concentration level. Extracts were prepared by grinding with the help of mixer grinder and mortar and pestle with equal amount of distilled water (1:1 wt/vol). The extract then filtered through muslin cloth to get the stock solution of 100% concentration and sterilized by autoclaving at 110°C for 10 minutes.

In-vitro test
The in-vitro test of different botanical extracts and fungicides against Bipolaris sorokiniana was done at the laboratory of NPPRC, Khumaltar, Lalitpur during 2019. Experiment was done in completely randomized design (CRD) by poisoned food technique (Schimitz, 1930). PDA media in lukewarm stage were amended with desired concentration of different botanical extracts and fungicides before pouring into 9 cm petri plates. PDA plates without botanical extract or fungicide were also used as control. Then, inoculation of 5 mm circular discs of pathogen (B. sorokiniana) (24 hrs. after pouring amended PDA media in petri plates) was done with help of sterile cork borer from one week old culture at the center of each PDA plates under aseptic condition. The experiment was done in three replications and the plates were incubated at 25°C. Colony diameter i.e. radial mycelial growth of the fungus was recorded daily for one week by Vernier Caliper and percent growth inhibition (I %) of B. sorokiniana was calculated by using the formula given by Vincent in 1947. I % = (C-T)/C×100 Where, C = Colony diameter in control plates (PDA only) T = Colony diameter in treated plates (PDA with plant extracts or chemicals) Statistical analysis R Studio software using R-3.6.3 and the agricolae package ver. 1.3-3 was used for the analysis of variance (ANOVA) to test the significance of treatments effect on mycelial growth of B. sorokiniana. Duncan's multiple range test (DMRT) was done to compare the means of significant treatments at 1% level of significance.

RESULTS
The different botanical extracts at different concentrations inhibited the mycelial growth of B. sorokiniana significantly (p<0.01). The highest radial mycelial growth was observed in control and neem leaf extract (9 cm) at 5% concentration and second highest was in case of neem (8.64 cm) at 10% which were statistically non-significant to control (9 cm) where other treatments were found significantly different with control. The lowest radial mycelial growth was observed in garlic extract (4.24 cm) at 15% concentration which was statistically at par to bojho extract (4.28 cm and 4.47 cm) at 15% and 10%, and garlic extract (4.67 cm) at 10% concentration levels (Table 1).
All the tested botanical extracts showed control over the pathogen, B. sorokiniana growth in varying degrees except neem leaf extract at 5% concentration (Table 1 and Figure 1). Garlic clove extract at 15% showed highest mycelial growth inhibition percentage (52.85%) which was followed by bojho (52.48% and 50.33%) at 15% and 10%, garlic (48.07%) at 10% concentration and they were statistically similar to each other. The lowest inhibition percentage was observed in neem (0%) at 5% concentration which was statistically different with all other treatment combinations. The mycelial growth inhibition percent of neem, garlic, eucalyptus, bojho and asuro at 15% concentration level on PDA were 9.59%, 52.85%, 37.07%, 52.48% and 29.89% respectively (Table 1). From regression equation (Figure 3) between concentration of neem extract and mycelial growth inhibition percentage revealed that more than 99% of the variation in increased inhibition percentage could be explained by the increase in concentration followed by 87% of garlic, 79% of eucalyptus, 81% of bojho and 92% of asuro respectively.

Figure 1: Effect of different botanical extracts at different concentration on mycelial growth of B. sorokiniana
As compared to control, all the tested fungicides were found effective in controlling the growth of B. sorokiniana. The different fungicides at different concentrations inhibited the mycelial growth significantly (p<0.01). The highest radial mycelial growth was observed in control (9 cm) which was statistically different with all other treatment combinations. The lowest radial mycelial growth was observed in tilt (0 cm) at all concentrations (25 ppm, 50 ppm and 100 Journal of Agriculture and Natural Resources (2020) 3(2): 296-305 ISSN: 2661-6270 (Print), ISSN: 2661-6289 (Online) DOI: https://doi.org/10.3126/janr.v3i2.32534 ppm) followed by Sajha (1.82 cm) at 100 ppm, saaf (2.38 cm) at 100 ppm, sectin (2.70 cm) at 100 ppm and so on ( Table 2).

DISCUSSION
Due to the development of resistance by plant pathogens against many fungicides, a new and alternative fungi-toxic substance from different sources including botanicals need to be investigated. The results indicated that various plant extracts tested inhibited the mycelia growth of B. sorokiniana as compared to control. As the concentration of botanical extracts increases, the efficacy for controlling the mycelial growth of B. sorokiniana was also increased. Similar results on antifungal activity of different plant extracts has been reported by several workers (Hasan et al., 2012;Perello et al., 2013;Yasmin, 2016). At 15% concentration, garlic clove extract significantly checked the growth of pathogen followed by bojho at 15% and 10%, and garlic at 10%. Many researchers found, extract of garlic bulbs were effective against B. sorokiniana (Miah et al., 1990;Khan and Fakir, 1995;Rahman et al., 1999;Ahmed et al., 2000;Hasan et al., 2005;Hoassain et al., 2005;Patidar, 2014). From the present study it can be concluded that garlic and bojho extract (both having more than 50% mycelial growth inhibition) were effective botanicals in controlling the growth of B. sorokiniana. Hasan et al. (2012) studied the efficacy of different botanical extracts on B. sorokiniana by poison food technique under in-vitro condition and found that garlic extract at 15% concentration was superior to all other treatments with maximum mycelial growth inhibition of 67.5%. Subedi et al. (2019) also reported that bojho (Acorus calamus) was the most effective botanical to suppress the growth of Exerohilum turcicum. Neem leaf extract was the least effective plant extract among the tested botanicals to inhibit the growth of B. sorokiniana while others gave at least more than 20% growth inhibition at all concentrations on PDA.
Among the tested fungicides, tilt (propiconazole 25%) was the most effective (complete mycelial growth inhibition at all concentration) against the pathogen which was also supported by the finding of Hasan et al. (2012) who found propiconazole, hexaconazole and difenoconazole+propiconazole had complete inhibition on growth of B. sorokiniana. Kavita et al. (2017) evaluated fifteen fungicides in-vitro against B. sorokiniana of Barley and found that propiconazole at 0.1% and 0.05% was the most effective in controlling mycelial growth of pathogen. Several researchers also suggested foliar spray with triazoles fungicide for the effective management of foliar blight in wheat (Bhatta et al., 1997;Naitao and Yousan, 1997; Mahto, 1999). Even at 25 ppm, tilt has complete mycelial growth inhibition so it is better to check at even low concentration to find the minimum dose for controlling the pathogen. The bavistin (carbendazim 50%) didn't inhibit the growth of pathogen and this was in agreement with the findings of Giri et al. (2001), Kavita et al. (2017) and Angdembe et al. (2019). In contrast to our findings, Samia et al. (2015) reported 100% and 77% mycelial growth inhibition at 300 ppm and 200 ppm of bavistin respectively. So, there is some contradictions whether the fungicide carbendazim effect on the pathogen or not.

CONCLUSION
All the tested botanicals and fungicides exhibited fungicidal action and significantly inhibited mycelial growth of B. sorokiniana as compare to control under in-vitro. Also, based on the evaluation, it can be concluded that mycelial growth inhibition percentage increases with the increase in concentrations of all botanicals and chemical fungicides. In case of botanicals, the highest mycelial growth inhibition percentage was found by the application of garlic clove extract (52.85%) at 15% which was followed by bojho (52.48%) at 15% concentration while tilt (100%) was the most effective fungicide which completely inhibits the mycelial growth of the pathogen at all concentrations. Also, fungicides viz. sajha, saaf and sectin had at least 70% mycelia growth inhibition at 100 ppm concentration and were effective in controlling the pathogen. This findings needs further verification under in-vivo condition to see any phytotoxic effect on the host and find out the degree of control over the pathogen. Also, further investigation needs to be done along with other treatments combination to confirm the result and generate more information on controlling the pathogen.


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Probiotics as a Biocontrol Agent in Management of Post Harvest Diseases of Mango

Mango (Mangifera indica L.) is the most important horticultural crop in Maharashtra as well as in India. However, this crop is well acclimatized to tropical and subtropical climatic regions. Post harvest losses caused by micro organisms are reported as from 30 to 35% losses with reduction in quality and quantity of marketable fruits and accounts for millions of dollars in perishable produce every year. Over the years, the plant protection was achieved by use of synthetic chemicals. However, use of probiotics for control of post harvest diseases has taken momentum in recent years in management of post harvest plant pathogens. The post harvest pathogens like Colletotrichum, Alternaria, Aspergillus, can be checked by post harvest probiotic sprays. Which include use of commercial probiotics (Prowel,Flora and Vbact) and Probiotic isolates, which are isolated and cultured from curd, fermented dosa material, fermented jowar and bajra flour. The studies on use of probiotics in the management of post-harvest diseases of mango conducted at Mahatma Phule Krishi Vidyapeet, Rahuri, Ahmednagar during the period 2017-2019. suggest that the post-harvest pathogens particularly Colletotrichum, Alternaria can be managed as a postharvest pathogen by sprays of probiotics under in-vitro conditions when the load of inoculums of Original Research Article Greeshma et al.; CJAST, 39(2): 85-92, 2020; Article no.CJAST.54905 86 these post-harvest pathogens are less on the fruits and the probiotic spray resulted in increase in the TSS (Total Soluble Sugars) brix (0.8-3.2) in the fruits and also be used to increase the shelf life of the fruits and to keep them as fresh for long time. The efficacy of probiotics against postharvested pathogens indicates that probiotics can be used in management of post harvest diseases and they can form an integral part of organic farming system.

INTRODUCTION
Fruits are the important component of human diet. They are considered as perfect food as they are the rich source of vitamins, minerals, enzymes and fiber. They play a vital role in human nutrition only. Fruits along with vegetables are rich sources of vitamins (A, B complex and C) and minerals (calcium, iron and phosphorus) in diets to keep human health in condition and are termed as 'protective foods'. Mango (Mangifera indica L.) belonging to family Anacardiaceae is one of the most popular of all tropical fruits. Mango fruits are rich in amino acids, carbohydrates, fatty acids, minerals, organic acids, proteins and vitamins. A fully ripe mango rich in vitamin A (beta carotene is considered a cancer-fighting agent. It is also high in vitamin C (27.7 mg) and is a good source of carbhohydrates (17 gm) and fibre too. Mangoes are good for the kidneys, aid digestion, provide excess body heat and favour relieve clogged skin pores and reduce cysts [1].
In India, Mango is cultivated over an area of 2262.77 thousand mha. with a productivity of 8.7mt (horticulture statistics division, 2018) and total production of 19686.93 thousand mt. Maharashtra state occupies 157.07 thousand ha area with production of 514.87 thousand million tonne and ranks tenth in production of various mangoes but stands unique in production of Alphonso in the regions of Ratnagiri, Raigad, Sindhudurg. Devgad and Pune is also playing an important role in production of rich mangoes on the sides of Sholapur highway.  [3]; Okigbo and Osuinde [4]. Among them in India, C.gloeosporioides causing large spreading lesions on the fruit surface. Losses in the field due to this disease have been estimated to be 2 -39% [5]. During storage, losses extended up to 51.70%. L. theobromae causes black lesions around the base of the pedicel [5]. Losses due to stem end rot of mango were reported to be around 7% and transit rot was 2% the losses due to post harvest pathological disease were found from producer to consumer was 27% [6]. Mango post-harvest diseases include anthracnose (C. gloeosporiodies), stem end rot (Botrydiplodia theobromae Pat.), Aspergillus rot (Aspergillus niger (L.) Van Tieghem) and Alterneria rot and the incidence was found in range of 40.00-90.00 per cent, 16.00-86.60 per cent, 3.33-53.30 per cent and 16.76 per cent respectively [7].
Microorganisms causing post-harvest diseases resulting in losses accounts for millions of dollars in perishable produce every year [8]. To minimize these losses it is very necessary to control the post-harvest diseases by biological, chemical and physical treatments [9]. The chemical control which includes application of therapeutical fungicides as pre-harvest treatment under field conditions, posses a serious threat of residues of these chemicals on consumable product. Therefore a novel method of control of these post-harvest diseases is a need of the day to save our produce. The use of probiotic in the management of human, animal and aquaculture diseases has gained momentum in recent years; however, their use in the management of crop plant disease is growing in recent years with increase in health consciousness of people.
In 2002, an FAO/WHO joint panel defined probiotics as live microorganisms, which when administered in adequate amounts, confer a health benefit on the host (FAO/WHO, 2002). Most probiotics are bacteria, the most common type among is lactic acid bacteria (LAB) but a few molds and yeasts can also be used as probiotics [10].
A good probiotic must be an organism that is capable of exerting a beneficial effect on the human hosts, increase growth or resistance to disease; it must be nonpathogenic and non-toxic; it must be capable of surviving and metabolizing in the gut environment by resisting the low pH of the stomach, enzymes, organic and bile acids present in the intestine and it should be stable under storage and field conditions [11]. Genera commonly used as probiotics include the bacterial-Lactobacillus, Bifidobacterium, Lactococcus, Carnobacterium, Enterococcus, Streptococcus, Pediococcus, Propionibacterium, Leuconostoc, and Bacillus species, Saccharomyces yeasts and Aspergillus molds [12]. They are beneficial organisms because they can provide beneficial effects on human such as altering the intestinal microflora balance, inhibiting the growth of pathogenic bacteria, synthesizing and enhancing the bioavailability of nutrients, promoting good digestion, reducing the effect of allergens, boosting immune function, lowering cholesterol, stimulating the immune system, [13]. Probiotics occur naturally with in the fermented food product such as yoghurt, sauerkraut, kefir, soybean-based miso and natto and cabbage kimchee. As the probiotics are consumed orally and beneficial to the human health their presence on the consumable fruits wont have any harmful affect on the human. As the probiotics are known to control pathogenic infections in human and are safe microbial formulations, there use to control pathogenic infection in post-harvest food produce is interesting to study and therefore the present investigation is taken up.

Isolation of Pathogen
The collected diseased samples of mango were surface sterilized with 1% sodium hypochloride solution and dried for 2-3 minutes and partly diseased portion with some partly healthy portion was cut with the help of sterilized blade under aseptic conditions in laminar air flow and the sterilized bits were kept on potato dextrose media plates. The inoculated Petri plates were then incubated at 25±2°C for 3 days. The fungal growth obtained was purified by mycelial tip isolation method and further it was identified upto genus level based on their characteristics.

Probiotic Isolates
Probiotic isolates were isolated from respective samples such as fermented jowar, bajra, dosa flour on Malt Extract Agar and From curd on Nutrient Agar media by streaking, they are named as Probiotic isolate I an edible yeast culture obtained from jowar flour, Probiotic isolate II an edible yeast culture obtained from bajra flour, Probiotic isolate III an edible yeast culture obtained from dosa flour, Probiotic isolate IV an Lactobacillus obtained from curd.

Commercial Probiotics
The Commercial probiotics which are easily available in the market and used in the study are Flora (

Pathogenicity
The pathogenicity test of isolated fungal pathogens were carried out by undertaking the inoculation experiments on healthy fruits of mango. These healthy fruits were washed in tap water, followed by spray of disinfectant 1:1000 sodium hypochloride (NaOCl) solution and were immediately washed with sterile water to remove the traces of disinfectant. Seven days old cultures of each isolate having good conidial and mycelial growth were suspended in sterilized water for preparation of inoculum spray suspension. The fruits were sprayed with respective fungal spore suspension with the hand sprayer under aseptic conditions and kept at ambient temperatures in the desicator for growth and infection of post-harvest pathogen. The type of symptoms developed/ fungal growth developed on the fruits were recorded eight days after inoculation.

In vitro efficacy of probiotics against the post harvest pathogens
The efficacy of Probiotics on pathogens was tested in vitro conditions by using dual culture Agar plate technique [14] a 5 mm disc mycelium inoculated on centre of petri dish containing PDA medium. In parallel a loop full of probiotic isolates and commercial probiotics were streaked 2.5 cm away one either sides of disc in the same plate. Paired cultures were incubated at 27-29ºC for 7 days. After 7days, growth diameter of the fungus (distance between the point of inoculation of fungal disc and actively growing edges of the fungus) was measured in both control

In vitro effect of probiotics against diseased fruit
A 2% sugar solution prepared by mixing 20 gm of sugar in one litre of water. In it one gm of probiotic Powder from respective probiotic sachet was added. This solution was incubated at 27±2ºC in BOD for 3 days for the growth of probiotic organisms. The suspension thus obtained was used for application on fruits. In case of probiotic isolates obtained the solution were prepared by taking one litre distilled water having 2 per cent sugar. A growth of individual probiotic I, II, III and IV from a single tube was added to this solution and incubated at 28 ± 2ºC in BOD for the growth of probiotic organisms for 3 days. To study the antagonistic effect of probiotics on post-harvested fruits the experiments were laid out in vitro conditions. The harvested mango fruits were brought to laboratory. These were washed with distilled water and dried. These fruits were then spray inoculated with suspension of respective post-harvest pathogen and again air dried. The respective probiotic suspension was sprayed on these pathogen inoculated fruits were air dried and kept at room temperature. The fruits sprayed with only with pathogen but no probiotic served as control. The observation was taken up to 10 days for growth of pathogen.

In vitro Efficacy of Probiotics against the Post Harvest Pathogens
The efficacy of Probiotic isolates viz., isolate I, II, III and Lactobacillus and Commercial probiotics viz Flora, Prowel, V bact were evaluated against three test post-harvest pathogens viz., Colletotrichum, Alternaria and Aspergillus obtained from mango by employing dual culture agar plate technique.
The results depicted in (Table 1) show that both Probiotic isolates and Commercial probiotics did not form any specific inhibition zone but the Probiotic isolates were comparatively inhibiting the pathogen slowing down the growth. Among the applied probiotic sprays on mango pathogens the Probiotic isolate -IV was more effective inhibiting the growth of colletotrichum up to 70.99% and Probiotic isolate-II was effectively inhibiting the growth of Alternaria up to 67.41% and Probiotic isolate -I was effective against Aspergillus by inhibiting the growth up to 48.76%. Among the Commercial probiotics the Commercial probiotic-III was effective in inhibiting the growth of Colletotrichum and the Commercial probiotic-I was effective against Alternaria compared to others.

Performance of Probiotics in the Management of Post-harvest Pathogens of Mango Fruits
The freshly harvested mango fruits were sprayed separately with individual inoculums of post harvest pathogens Colletotrichum, Alternaria and Aspergillus, which are the post harvest pathogens associated with mango. These pathogen suspension were sprayed on mango fruits and were subsequently sprayed with the probiotic suspension of Prowel, Flora, Vbact and Probiotic isolate after one hour. The treated fruits were kept in room temperature and were observed for the infection of test pathogens. The results (Table 2) indicated that none of the probiotic was able to control the postharvest pathogen infection of Aspergillus and there was a full growth of the post-harvest pathogen on fruits. The Probiotic isolates and Commercial probiotic I were able to control Colletotrichum and Probiotic isolates and Commercial probiotic I and II was effective against Alternaria. The results (Table 3) depict that when the fruits were treated by combining all Probiotic isolates they were more effective in inhibiting the pathogen.

Effect of Probiotic Application on Fruit TSS (Total Soluble Sugars) Content in Mango
The effect of application of Probiotic isolates and Commercial probiotics was seen on the fruit TSS content of Mango. For this the harvested fruits of mango were obtained and were sprayed with suspension of probiotics. The sprayed fruits were kept at room temperature and the TSS     The Probiotic Lactobacillus treated fruits showed inhibiton of 48% on Post harvest pathogens in Papaya and Banana and also it increased the shelflife of the fruits [15,16] and the probiotic formulations and commercial probiotics were effective in controlling post harvest diseases in grapes when the inoculum load was less and also reported that it improves the quality of Grape fruits [17]. In the same lines the present research was taken up. When the plants are treated with probiotic bacteria Paraburkholderia fungorum, Bacillus amylolequefaciens improved the antioxidants content, growth and yield in strawberry [18]. Probiotic microorganisms apply the mechanism of secreting a variety of antimicrobial compounds, including organic acids, hydrogen peroxide, bacteriocins and biosurfactants, that inhibit the growth of pathogenic bacteria; however, bacteriocins have stronger antimicrobial activity against pathogens under acidic conditions [19].

CONCLUSION
The studies on use of probiotics in the management of post-harvest diseases of mango suggested that the post-harvest pathogens particularly Colletotrichum, Alternaria, can be checked by the sprays of probiotics under in vitro conditions when the load of inoculums is low. The probiotics can be used to increase the TSS content as well as the shelf life of the fruits and to keep them fresh for a longer time. The efficacy of probiotics against post-harvested pathogens indicated that probiotics can be used in management of post harvest diseases and they can form an integral part of organic farming systems and also integrated disease management.

DISCLAIMER
The products used for this research are commonly and predominantly use products in our area of research and country. There is absolutely no conflict of interest between the authors and producers of the products because we do not intend to use these products as an avenue for any litigation but for the advancement of knowledge. Also, the research was not funded by the producing company rather it was funded by personal efforts of the authors.


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KNOWLEDGE AND ADOPTION OF PRODUCTION PRACTICES AMONG CHICKPEA GROWERS OF ANDHRA PRADESH

: India is the largest producer of pulses in the world. Pulses ensures food security with its rich source of protein. To feed the growing population of India, the production of pulses also need to be increased. Hence, in order to improve the production and productivity of pulses, the knowledge and adoption level of pulse growers should be learnt to understand the technological gap exists in between them. Among various pulse crops, chickpea has been widely cultivated. Thus, the knowledge and adoption level of production practices on chickpea among the chickpea growers of Kurnool, Andhra Pradesh was studied. Twelve villages from six mandals of three blocks of Kurnool district constitute the 120 chickpea growers. The primary data were gathered with the help of structured interview schedule through persona interview during 2019-2020. The findings revealed that chickpea growers had medium level of knowledge and adoption of production practices of chickpea because of medium level of innovative proneness, farm power and social participation. Hence, it was recommended to conduct training programmes and awareness campaigns to improve the knowledge and adoption of various production practices, which in turn increase the production and productivity of chickpea. and adoption level, Production practices, Andhra Pradesh.

INTRODUCTION
Agriculture serves as the "backbone of India", as it contributes 18 per cent of its income to GDP of our Nation and additionally provides employment to a more than half of the Indian population (Madhusudhan, 2015). Simultaneously, evolution and availability of farm technologies led to increase in agricultural growth. Among the highly populated countries, India secures second rank (Census, 2011). In order to feed the growing population of India, it becomes evident to ensure food security, which can be done through improving productivity of pulses.
Pulses are regarded as the important food crops because of their high protein content.
Haryana. The chickpea in India is cultivated over an area of 28.28 Million hectares and yields about 22.995 Million tines with productivity of 767 kg/ha (Ministry of India, 2016-17). In addition to this, it can be understood that area and production of pulses contribute 30 and 37 per cent to Nation respectively.
Madhya Pradesh serves as the largest producer in the country (40 % of total production), while Rajasthan (14%), Maharashtra (10%), Uttar Pradesh (9%), and Andhra Pradesh (7%) contribute respectively. In Andhra Pradesh, chickpea was mostly cultivated in districts of Kurnool, Prakasham and Guntur; over an area of 6.38 lakh hectares with an annual production of 9.37 lakh tones with a productivity of 1469 Kg per hectare. Though the area under chickpea was high, it does not meet the demand of people due to low production and productivity. The chickpea yield had remained stagnant over a long time and it depicts that there is a gap between the knowledge and adoption of chickpea production practices. Hence, a detailed study on the knowledge and adoption behavior of chickpea growers on the production practices helps to understand the extent of knowledge the farmers possess and the extent of adoption. chickpea growers had medium annual income from medium size of land holding (55%). Padiyar

Statement of the Problem
Kurnool is one of the districts of Andhra Pradesh, where chick pea was found to be the main crop. Even though, the area under cultivation of chickpea is constantly increasing in Kurnol, the production and productivity remains the same, even after the several efforts by govt.
functionaries and reputed NGOs. In turn, it depicts that there is a gap between the knowledge and adoption of chickpea production practices. Hence, a detailed study on the knowledge and adoption behavior of chickpea growers on the production practices helps to understand the extent of knowledge the farmers possess and the extent of adoption. With this background, the following objectives were formulated. The objectives of the study were as follows, • To ascertain the socio-economic profile of the respondents.
• To access the knowledge and adoption of recommended chickpea production practices by the respondents.

METHODOLOGY
The present study was conducted at Kurnool district of Andhra Pradesh. Kurnool district was purposively selected because of the adequate concentration of chickpea growers residing in the district. Out of 14 taluks in Kurnool district, Banaganapalle taluk was selected purposively.
Among the 5 mandals in Banaganapalle taluk, sanjamala, owk and koilakuntla mandals were randomly selected (Random sampling method). From each mandal, two villages constitute six villages under study area. The farmers whose main crop was chickpea were regarded as chickpea growers and the list of chickpea growers were gathered with the help of RAEO"s and other government officials. From each of the village, 20 chickpea growers were randomly selected, thus make 120 chickpea growers as respondents. The secondary data was gathered from RAEO"s, other government officials, survey reports, available literature and previous studies.
Meanwhile, the primary data were gathered from the chickpea growers with the help of structured interview schedule through personal interview method during 2019-2020.

RESULTS AND DISCUSSION
The gathered data was tabulated, distributed and analyzed with suitable statistical tools.  of the chick pea growers never participate in youth club, followed by occasionally participation (10%) and regular participation (5%). As far as participation, 82.50 per cent never participate in self help groups, where 10.83 per cent and 6.66 per cent of chick pea growers participate occasionally and regularly. Further, 92.50 per cent of the respondents "Never" participate in Other organizations (like Dairy cooperatives, Saving groups) followed by "Occasional" (4.16%) and "Regular" (3.33%).
Among the total chickpea growers, most of them had medium farm power (60.83%), followed by high farm power (20.83%) and low farm power (18.33%) per cent respectively.
More than half of the farmers had medium level of economic motivation (55%), followed by low    The possible reason might be the fact that, medium level of innovative proneness, farm power and social participation led to increased rate of adoption on various production practices in cultivation of chickpea. Similar findings were reported by Sharma et.al., (2015).

CONCLUSION
From the present study, it can be learnt that majority of the chickpea growers in Kurnool district were middle aged with primary school education and possessed medium size of family with marginal size of land holding. Subsequently, they had medium farm power which earns high annual income. In addition to this, most of the chickpea growers member in various social organizations with low extension contact and subscription for various mass media sources. They also had medium level of economic motivation, innovative proneness, risk orientation and resulted in medium level of knowledge and adoption of various production practices of chickpea.
Thus, it is also recommended to conduct more training programmes and awareness campaigns to increase production and productivity of chickpea, since it remains as the major crop of Kurnool.
Eventually, new policies to provide subsidy for pulse growers should be implemented.


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A EFFECT OF STRENUOUS PHYSICAL ACTIVITY ON THE SUM OF LEUKOCYTES IN WISTAR STRAIN MALE MICE

This research aims to determine the effect of strenuous physical activity on the sum of leukocytes. The study used experimental with a posttest only control group design approach. The study subjects of 10 male mice were randomly divided into two groups. The control group was not given heavy physical activity and the treatment group was given heavy physical activity. On the 27th day of blood retrieval for examination of the sum of leukocytes in the Integrated Biomedical Laboratory, in July-August 2020. The mean on the sum of leukocytes increased in the treatment group compared to the control group. The Mann Whitney test showed that the sum of leukocytes in the control group was significantly different from the treatment group with a value of p = 0.009 (p <0.05). More research is needed on strenuous physical activity in IL-6 levels and calculate leukocytes as one of the study boosters. This research can be used as a reference in the control of the immune system of the body Article History: Received 7 Oct 2020 Revised 14 Oct 2020 Accepted 16 Oct 2020 Available online 18 Oct 2020 ____________________ Keyword: strenuous physical activity, leukocytes, immune system

INTRODUCTION
Physical activity is part of the body's metabolic processes that produce free radicals and reactive oxygen species (ROS). Strenuous physical activity to fatigue will be an imbalance between the production of free radicals and the body's antioxidant defense system known as oxidative stress. (Harahap & Pahutar, 2018) Oxidative stress results in tissue damage that can result in an increase in the sum of leukocytes in maintaining the condition of the immune system. (Goleman, Daniel;Boyatzis, Richard;Mckee, 2019) An international athlete is taken very seriously in the face of the game. The standard training program is arranged 5-6 times a week, sometimes also having to do morning and afternoon exercises while in the training center. Strenuous physical activity will lead to suppression of immunity function in athletes so that it will be easier to experience infection. (Hayati, 2014) Previous research found 33% of 140 marathon runners suffer from an upper airway infection after a match, while the incidence of infection at control is only 15%. The incidence of airway infections in marathon runners apparently increased sixfold after the game. (Harahap & Pahutar, 2018) Other research has also shown that exercise with heavy intensity also increases the mobilization of neutrophils and monocytes in circulation. (Hayati, 2014) Excessive oxygen consumption in strenuous physical activity causes complex reactions in the body and results in free radical products. (Sinaga, 2007) All free radicals from such oxygen very quickly damage the tissues. One of them is tissue damage that results in leukocytes hissing into the damaged tissue which then affects the damaged tissue so that mobility of the number of leukocytes in the blood increases. (Salwati, 2016) Physical activity is a movement of the limbs that causes energy expenditure that is important for the maintenance of physical, mental health and maintaining the immune system in the body. Heavy physical activity can trigger an imbalance between the production of free radicals and the body's antioxidant defense system known as oxidative stress. (Rahman, 2018) Oxidative stress is often associated with tissue damage that triggers inflammatory reactions. This inflammation will give rise to an inflammatory response in the body to inflamed organs. The inflammatory response results in the onsanity of leukocytosis or an increase in the number of leukocytes to boost the immune system in maintaining the condition of the body. Leukocytosis is seen as a marker of inflammatory activity. Leukocytes are blood cells that play a role in the body's defense system from attacks of foreign substances and abnormal cells. (R.A. Nawawi, Fitriani, B. Rusli, 2016) Maximum weight gain during physical training or severe fatigue was found to be a change in the sum of leukocytes in the blood bank, which is thought to be the cause of the increased incidence of airway infections, due to the suppression of immunity function, resulting in a decrease in the appearance of athletes.

METHODS
This research uses laboratory experimental with posttest only control group design approach. Samples are obtained by allocating groups based on random sampling allocation. Male mice strain wistar as many as 10 tails that are 8 weeks old, weighing 150-200 grams adapted for 7 days then divided into 2 (two) groups namely the control group and treatment group. The control group was not given heavy physical activity and the treatment group was given the strongest physical activity in the form of a swimming pool until it nearly sank for about 30 minutes for 27 days. An examination of the number of leukocytes carried out on the 28th day was carried out at Integrated Biomedical Laboratory (IBL) faculty of medicine UNISSULA. The study sampled 0.5 ml mouse blood taken from the orbital sinuses then mixed with EDTA 10% as much as 5 microns examined with Haematology Analyser.
The average leukocytes level data is presented descriptively in the form of a table. Then the data in the normality test with the Shapiro Wilk test and the data homogeneity test with the Levene test. The distribution of data on the number of leukocytes obtained is abnormal and homogeneous, so continued with the nonparametric test of the Kruskal Wallis p<0.05 test followed by a two-group non-parametric differentiation test using the Mann Whitney test.

Results
Research into the influence of heavy physical activity on the sum of leukocytes in male mice of the wistar strain has been conducted for 27 days. The results of the study are listed in table 1.  Table 1 shows that the average number of leukocytes in the control group is lower than the recall treatment group across the entire leukocytes group based on wilk shapiro tests showing abnormally distributed data (p=030). While the homogeneity test using levene test the result is homogeneous (p>0.05) so that it uses a non-parametric test Kruskal Wallis. Wallis' Kruskal test results showed significant differences between the groups (p=0.009). To find out which different groups meaningfully performed the Mann Whitney test as presented in table 2. indicates the number of leukocytes in the control group (K) there was a significant difference to the first treatment group (P1) (p=0.009). Based on the above data, it can be concluded that heavy physical activity has a significant effect on the increase in the sum of leukocytes in male mice of the wistar strain.

Discussion
The increase in the sum of leukocytes in the sample is due to the movement of strenuous physical activity so that the body experiences fatigue which then consumes excessive oxygen which can result in a high number of leukocytes cells. (Arulselvan et al., 2016) In physiological conditions, free radicals form 5% of oxygen consumption and can be neutralized by antioxidants in the body. However, if the rate of free radical formation increases by more than 5% due to being triggered by heavy and exhausting physical activity, then the capacity of antioxidant defense systems cannot neutralize these excess free radicals. This imbalance can cause oxidative stress and stimulate leukocyte cell activity. (Khoirurohmah, Pusputasari, & Isro'aini, 2018) Excessive oxygen consumption in heavy physical activity causes complex reactions in the body and results in free radical products. All free radicals from such oxygen very quickly damage the tissues. One of them is tissue damage that results in leukocytes hissing into the damaged tissue which then affects the damaged tissue so that mobility of the number of leukocytes in the blood increases. (Salwati, 2016)

CONCLUSION
This study can be concluded that strenuous physical activity can increase significantly against the number of leukocytes.

ACKNOWLEDGEMENTS
Thanks to the Clinical Pathology laboratory of the Faculty of Medicine, Gadjah Mada University (UGM). Thanks also to the Integrated Biomedical Laboratory (IBL) of Sultan Agung Islamic University (UNISSULA) of Semarang and Haematology Laboratory of the Faculty of Nursing and Health Sciences (FIKKES) of Muhammadiyah University Semarang (UNIMUS). should be described. All sources of funding for the work must be acknowledged, both the research funder and the grant number (if applicable) should be given for each source of funds