Water relations, growth and physiological response of seven strawberry cultivars (Fragaria×ananassa Duch.) to different water availability

Martínez-Ferri, Elsa; Soria, C.; Ariza, M.T.; Medina, J.J.; Miranda, L.; Domíguez, P.; Muriel, J. L.

doi:10.1016/j.agwat.2015.08.014

Cited by 40 publications

(48 citation statements)

References 31 publications

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“…Fleecing reduced total yield and class 1 fruit yield, and increased rates of stomatal conductance. This was linked to greater air temperatures and soil moisture content and is indicative of heat stress (Martínez-Ferri et al 2016). Embedded autonomous sensors and coupled weather models may therefore also be used by farm managers to prevent acute problems caused by growing conditions, such as adding fleeces only during the periods when frost is likely or opening doors and vents to protect plants if severely high temperatures are anticipated.…”

Section: Agronomic Implicationsmentioning

confidence: 99%

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A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Lee

Monteiro²,

Barclay

et al. 2019

Preprint

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Predicting harvest timing is a key challenge to sustainably develop soft fruit farming and reduce food waste. Soft fruits are perishable, high-value and seasonal, and sales prices are typically time-sensitive.In addition, fruit harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. A novel approach for predicting soft fruit phenology and yields was developed and tested, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR). Fleeces were added to a subset of the plants to generate additional withinpolytunnel variation. Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic growing conditions. After 10,000 iterations, machine learning revealed that RH was the optimal factor informing the coefficients of these curves, perhaps because it is an integrative metric of air temperature and water status. Trigonometric models transformed weather forecasts, which showed a relatively low agreement with polytunnel air temperature (R 2 = 0.6) and RH (R 2 = 0.5) measurements, into more accurate polytunnel-specific predictions for temperature and RH (both R 2 = 0.8). We present a framework for using machinelearning techniques to calculate curve coefficients and parametrise coupled weather models which can predict fruit yields and timing to a greater degree of accuracy that previously possible. Dataloggers measuring environmental and yield data could infer model parameters using iterative training for novel fruit varieties or crop types growing in different locations without a-priori phenological information. At this stage in the development of artificial intelligence and networked microsensors, this is a step forward in generating bespoke phenological prediction models to inform and support growers.

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Section: Agronomic Implicationsmentioning

confidence: 99%

“…Reductions in sunlight, abrupt changes in air temperature and freezing, as well as periods of drought have also been associated with changes in strawberry yields (Ariza et al 2015;Martínez-Ferri et al 2016), whilst adequate soil temperatures are a crucial aspect of germination (Domínguez et al 2014).…”

Section: Introductionmentioning

confidence: 99%

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Lee

Monteiro²,

Barclay

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…K is the second most abundant element in plant tissues after N [22,23], and it helps enhance water uptake and fruit quality [24]. In addition to mineral fertilizers, water greatly contributes to the development of the strawberry fruits, leaves and othe organsf [25], and water shortages can lead to large losses of strawberry yield [26].…”

Section: Introductionmentioning

confidence: 99%

Optimal fertigation for high yield and fruit quality of greenhouse strawberry

Yong

et al. 2020

PLoS ONE

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Nitrogen (N), phosphorus (P), potassium (K), and water are four crucial factors that have significant effects on strawberry yield and fruit quality. We used a 11 that involved 36 treatments with five levels of each of the four variables (N, P, and K fertilizers and water) to optimize fertilization and water combination for high yield and quality. Moreover, we used the SSC/TA ratio (the ratio of soluble solid content to titratable acid) as index of quality. Results showed that N fertilizer was the most important factor, followed by water and P fertilizer, and the N fertilizer had significant effect on yield and SSC/TA ratio. By contrast, the K fertilizer had significant effect only on yield. N×K fertilizer interacted significantly on yield, whereas the other interactions among the four factors had no significant effects on yield or SSC/TA ratio. The effects of the four factors on yield and SSC/TA ratio were ranked as N fertilizer > water > K fertilizer > P fertilizer and N fertilizer > P fertilizer > water > K fertilizer, respectively. The yield and SSC/TA ratio increased when NPK fertilizer and water increased, but then decreased when excessive NPK fertilizer and water were applied. The optimal fertilizer and water combination were 22.28-24.61 g plant -1 Ca (NO 3 ) 2 �4H 2 O, 1.75-2.03 g plant -1 NaH 2 PO 4 , 12.41-13.91 g plant -1 K 2 SO 4 , and 12.00-13.05 L water plant -1 for yields of more than 110 g plant -1 and optimal SSC/TA ratio of 8.5-14. OPEN ACCESSCitation: Wu Y, Li L, Li M, Zhang M, Sun H, Sigrimis N (2020) Optimal fertigation for high yield and fruit quality of greenhouse strawberry. PLoS ONE 15(4): e0224588. https://doi.org/10.

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“…K is the second most abundant element in plant tissues after N [22,23], and it helps enhance water uptake and grain quality [24]. In addition to mineral fertilizers, water greatly contributes to the strawberry fruit content and leaf development [25], and water shortages can lead to large losses of strawberries yield [26].…”

Section: Introductionmentioning

confidence: 99%

“…Although studies have shown that all mineral fertilizers (N, P, and K) and water have effects on the yield and quality of strawberries, most of them only focused on either the effect of water [26–28] or the effect of fertilization [29–33]. The combined application of N, P, and K (NPK) fertilizers and water for high yield and good fruit quality is rarely reported [27,31,34].…”

Section: Introductionmentioning

confidence: 99%

Optimal fertigation for high yield and fruit quality of greenhouse strawberry

Yong

et al. 2019

Preprint

View full text Add to dashboard Cite

2 21 Abstract 22 Nitrogen (N), phosphorus (P), potassium (K), and water are four crucial factors that have significant effects on strawberry yield 23 and fruit quality. A quadratic regression orthogonal rotation combination experiment that involved 36 treatments with five 24 levels of the four variables (N, P, and K fertilizers and water) was executed to optimize the fertilization and water combination 25 for high yield and quality. SSC/TA ratio (the ratio of soluble solid content to titratable acid) was selected as the index of 26 quality. Results showed that the N fertilizer was the most important factor, followed by water and P fertilizer, and the N 27 fertilizer had a significant effect on yield and SSC/TA ratio. By contrast, the K fertilizer had a significant effect only on yield. 28N×K fertilizer interaction had a significant effect on yield, whereas the other interactions among the four factors had no 29 significant effects on yield and SSC/TA ratio. The effects of the four factors on the yield and SSC/TA ratio were ranked as N 30 fertilizer > water > K fertilizer > P fertilizer and N fertilizer > P fertilizer > water > K fertilizer, respectively. The yield and 31 SSC/TA ratio increased and then decreased when NPK fertilizer and water increased. The optimal fertilizer and water 32 combination was 22.28-24.61 g/plant Ca (NO 3 ) 2 ·4H 2 O, 1.75-2.03 g/plant NaH 2 PO 4 , 12.41-13.91 g/plant K 2 SO 4 , and 12.00-33 13.05 L/plant water for yields of more than 110 g/plant and optimal SSC/TA ratio of 8.5-14. Introduction 35Mineral fertilizers and water have a significant effect on crop yield [1-3]. However, the excessive application of fertilizers may 36 lead to soil and water pollution and become a serious threat to food safety [4,5]. Meanwhile, water scarcity is now a major 37 challenge in China [6]. Therefore, a good management of fertilization and water is increasingly required for agriculture in China. 38Strawberry is one of the most profitable fruit cultivars in China, which ranks first in total strawberry production worldwide 39 with a production of 1,801,865 tons in 2016, followed by United States and Mexico among a total of 79 countries [7]. Thus, large 40 amounts of fertilizers and water are needed for strawberry production in China. Consumers prefer strawberries with a sweet taste 41 [8,9]. The strawberry flavor is strongly correlated with the balance between the soluble solid content (SSC) and titratable acid 42 (TA) in ripe fruits [10,11], which are common quality indexes to assess sweetness and sourness [12]. The ratio of SSC/TA is 43 another effective parameter to determine fruit flavor [13,14]. The higher the SSC/TA ratio, the sweeter the fruit [15]. Therefore, 44 increasing strawberry production and enhancing fruit quality with high SSC/TA ratio and without environment pollution are 45 important goals in the field. 46Nitrogen (N), phosphorus (P), and potassium (K) are primary mineral fertilizers. N is the most limiting nutrient to crop nutrient is essential for photosynthesis [20], and it is requ...

show abstract

Water relations, growth and physiological response of seven strawberry cultivars (Fragaria×ananassa Duch.) to different water availability

Cited by 40 publications

References 31 publications

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques

Optimal fertigation for high yield and fruit quality of greenhouse strawberry

Optimal fertigation for high yield and fruit quality of greenhouse strawberry

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