Influence of Foliar and Ground Fertilization on Yield, Fruit Quality, and Soil, Leaf, and Fruit Mineral Nutrients in Apple

Amiri, Maryam; Fallahi, Esmaeil; Golchin, Ahmad

doi:10.1080/01904160801895035

Cited by 66 publications

(41 citation statements)

References 19 publications

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“…Heavy herbivory has been known to result in death of species and repetitive reproductive failure (Brooks and Matchett 2006;Todd 2006). Besides, overgrazing hampers seed germination and consequently inhibits plant growth and development further reducing vegetation cover, soil hydraulic conductivity, and nutrients available to plants (Amiri et al 2008). These findings corroborate those of Angassa (2014) who observed that 80% of herbaceous species exhibited a high vulnerability to grazing in the Borana region of Southern Ethiopia.…”

Section: Shannon-wiener Diversity Index Species Richness and Pielousupporting

confidence: 82%

“…Further, hoof action could have led to soil compaction hindering infiltration of water and consequently low plant growth (Mapfumo et al 2000). As reported by Amiri et al (2008), soil compaction also triggers depletion of soil organic matter, which hampers the growth of plant species. Defoliation, excretion, and seed dispersal by grazing animals influence plant diversity (Duncan 2005).…”

Section: Shannon-wiener Diversity Index Species Richness and Pieloumentioning

confidence: 99%

“…Grazing alters species composition within the rangeland ecosystems (Todd 2006;Wu et al 2009). High grazing pressure reduces the density of palatable plants forcing the animals to forage on species of low nutrition value (Amiri et al 2008). As such, most preferred plant species cease to grow, giving way for the proliferation of forbs (Chaichi et al 2005;Loydi et al 2012).…”

Section: Species Compositionmentioning

confidence: 99%

“…Plant defoliation by livestock and wildlife grazing around these piospheres increases palatable species' mortality, thereby reducing their ability to favorably compete with less palatable alien and exotic species (Hunt et al 2002;Shahriary et al 2012). Further, trampling reduces seed production and seedling establishment in the rangelands (Chaichi et al 2005;Amiri et al 2008). Grazers also change rangeland vegetation through deposition of urine and feces and by dispersing plant seeds and propagules (Shahriary et al 2012).…”

Section: Introductionmentioning

confidence: 99%

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Plant species composition and diversity depending on piospheres and seasonality in the southern rangelands of Kenya

et al. 2017

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Introduction: Scarcity of water in the rangelands of Kenya has led to the introduction of piospheres. Previous research has however produced contrasting results on the effects of piospheres and seasonality on ShannonWiener's diversity index, and therefore, this information is still deficient. In this study, the impact of these piospheres on plant species composition, diversity, and richness was assessed. Methods: Vegetation sampling was done during both the long rains (April) and the dry season (August). Three piosphere types (dam, trough, and a seasonal river) were studied using 0.25 m 2 quadrats to sample vegetation at intervals of 20 m along 100-m transects. Four 100-m transects were used per piosphere (north, east, south, and west directions). Shannon-Wiener's diversity index was used to determine species richness and composition. Twoway ANOVA was used to determine if piospheric distance had an effect on species diversity, richness, and evenness using GenStat 15th edition. Results: A total of 22 grasses and 29 forbs were recorded in the study area. The most abundant grasses near the piospheres were Eragrostis tenuifolia (12.9%) and Cynodon dactylon (10.6%). E. tenuifolia is an increaser species and was therefore most abundant in areas of severe grazing while C. dactylon has been known to be tolerant to grazing. The most abundant forbs were Crotalaria brevidens (37.5%). The Shannon-Wiener diversity index significantly increased (F = 25.07, P = 0.001) with distance from the three piospheres owing to the high grazing intensity near the piospheres and was significantly different between piospheres, being higher (F = 10.05, P = 0.001) at 20 m from the river (1.2 ± 0.1) compared to a similar distance from the dam (0.9 ± 0.1) and the trough (0.8 ± 0.2). This was probably because the trough was smaller in size compared to the other piospheres, thereby concentrating more grazing animals per unit area and causing a greater impact on plant species. Conclusions: The study demonstrated that species diversity, richness, and evenness were low near the piospheres due to heavy grazing. Similarly, the lowest species diversity, richness, and evenness were recorded near the trough compared to the dam and the river. This study therefore recommends that range reseeding and rest period be done to rehabilitate degraded areas and facilitate plant regeneration. Larger piospheres should also be used to minimize animal impact per unit area.

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Section: Shannon-wiener Diversity Index Species Richness and Pielousupporting

confidence: 82%

Section: Shannon-wiener Diversity Index Species Richness and Pieloumentioning

confidence: 99%

Section: Species Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Plant species composition and diversity depending on piospheres and seasonality in the southern rangelands of Kenya

et al. 2017

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“…In this research, we found out that a K/Ca ratio above 20 during harvest led to an increase in post-storage losses caused by diseases and disorders. Unlike some other studies, which reported the correlation between N/Ca ratio and losses during storage (Amiri et al 2008, Casero et al 2010, this study showed no such correlation for 'Ligol'. Therefore, N/Ca ratio seems to be useless for the prediction of storage losses in this cultivar.…”

Section: Resultscontrasting

confidence: 54%

The influence of harvest maturity and basic macroelement content in fruit on the incidence of diseases and disorders after storage of the ‘Ligol’ apple cultivar

Łysiak

2013

Folia Horticulturae

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Research was carried out during the 1999-2007 growing and storage season using 'Ligol' apples from trees grafted on M.26, and fertilised in accordance with recommendations for commercial orchards. The approximate optimum harvest date was determined mainly on the basis of starch index measurements and Streif index calculations, evaluated 7-8 times every 4-5 days each year. In addition to samples collected to determine OHD each year, there were four harvests of fruit intended for storage. The apples were stored in a cold storage room at 1-2°C and RH of around 90% for about five months. Following a storage period lasting the same number of days for each harvest, the quality of fruits was examined and the correlation between the concentration of minerals in apples and all fungal diseases and physiological disorders was calculated. In years with high precipitation in the period preceding the harvest, the share of fruits affected by fungal diseases and physiological disorders after storage depended more on weather conditions prevailing in the growing season than on the harvest date. The incidence of bitter pit increased with the number of days preceding OHD. Other recorded losses were caused by internal breakdown and superficial scald, but they were small and did not seem significant for 'Ligol' apples. Each characteristic mineral concentration in the fruit at harvest (N, P, K, Ca and Mg) was correlated with the incidence of physiological disorders and/or fungal diseases. The incidence of bitter pit and lenticel blotch pit and the sum of physiological disorders increased along with the increase in nitrogen concentration. The feature that best predicted the storability of 'Ligol' was the K/Ca ratio.

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Using artificial neural network in predicting the key fruit quality of loquat

Huang

Wang²,

et al. 2021

Food Science & Nutrition

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The formation and regulation of loquat fruit quality have always been an important research field to improve fruit quality, commodities, and market value. Fruit size, soluble solids content, and titratable acid content represent the most important quality factors in loquat. Mineral nutrients in abundance or deficiency are among the most important key factor that affect fruit quality. In the present study, we use artificial neural network (ANN) to explore the effects of mineral nutrients in soil and leaves on the key fruit quality of loquat. The results show that the ANN model with the structure of 12–12–1 can predict the single fruit weight with the highest accuracy (R2 = .91), the ANN model with the structure of 10–11–1 can predict the soluble solid content with the highest accuracy (R2 = .91), and the ANN model with the structure of 9–10–1 can predict the titratable acid content with the highest accuracy (R2 = .95). Meanwhile, we also conduct sensitivity analysis to analyze the relative contribution of mineral nutrients in soils and leaves to determine of the key fruit quality. In terms of relative contribution, Ca, Fe, and Mg content in soils and Zn, K, and Ca content in leaves contribute relatively largely to a single fruit weight, Mn, Fe, and Mg content in soils and the N content in leaves contribute relatively largely to the soluble solid content, and the P, Ca, N, Mg, and Fe in leaves contribute relatively largely to the titratable acid content of loquat. The established artificial neural network prediction models can improve the quality of loquat fruit by optimizing the content of mineral elements in soils and leaves.

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Influence of Foliar and Ground Fertilization on Yield, Fruit Quality, and Soil, Leaf, and Fruit Mineral Nutrients in Apple

Cited by 66 publications

References 19 publications

Plant species composition and diversity depending on piospheres and seasonality in the southern rangelands of Kenya

Plant species composition and diversity depending on piospheres and seasonality in the southern rangelands of Kenya

The influence of harvest maturity and basic macroelement content in fruit on the incidence of diseases and disorders after storage of the ‘Ligol’ apple cultivar

Using artificial neural network in predicting the key fruit quality of loquat

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