Dataset explaining the comparative seasonal crop load and harvest quality of guava upon pruning strategies
Description
The dataset explains the details on how pruning techniques significantly affect the seasonal variations on fruit availability and edible quality of guava under fluctuating sub-tropical weather conditions. The present pruning data also direct a way of enhancing lean season (off-season) harvest without sacrificing the main season yield and fruit quality. Over two consecutive years (2019-2020 and 2020-2021), the pruning treatments were assigned in triplicates where the same plants received the same treatments during observation period starting with spring pruning. Data on crop load like number of fruits and fruit yield plant-1 and fruit biochemical traits namely total soluble solids, titratable acidity, total sugars, vitamin C and fruit specific gravity were recorded. To assess the seasonal variations, data collection was performed continuously and grouped as rainy and non-rainy seasons. Irrespective of pruning techniques, rainy season had superior yield, whereas non-rainy harvests retained utmost fruit quality. Considering pruning time, plants reserved maximum harvestable fruits during rainy period under march pruning. Moreover, total soluble solids, total sugars, vitamin C and fruit specific gravity examined the best at non-rainy harvests under autumn pruning. Alongside, wet period exhibited superiority for yield over dry period when plants were pruned at 0 cm to 30 cm levels, but 45 cm pruning level showed differential results.
Files
Steps to reproduce
In two consecutive growing years (2019-20 and 2020-21), number of shoots and leaves plant-1, number of flowers and fruits plant-1, fruit yield (kg plant-1) and fruit physicochemical properties namely total soluble solids, titratable acidity, total sugars, vitamin C contents and fruit specific gravity were recorded. The collected data were categorized into four groups corresponded to the four quarters of the year. Number of flowers and fruits plant-1 was counted manually, fruit yield was measured using electric balance, and standard chemical analysis procedures and formula were followed to determine the fruit quality data at the laboratory. Every time, mean chemical measurements of ten random fruits was considered as single value. Hand held digital refractometer (PAL, ATAGO, Japan) was used to assess the TSS content of fruits. Chemical reagents namely NaOH, Phenolphthalein indication, 2, 6-dichlorophenol indophenol dye as well as Bertrand A, Bertrand B, and Bertrand C solutions were utilized for laboratory data analysis.