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Akhter, Javed; Mahmood, Khalid; Tasneem, M.A.; Naqvi, M. H.; Malik, Kauser A.

doi:10.1023/a:1022836916394

Cited by 14 publications

(6 citation statements)

References 33 publications

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“…Comparing future scenarios (Figure 3), during the first study period , droughts are expected to become overall more frequent and intense under both scenarios, in agreement with a growing body of literature (Dai, 2012; Akhter et al (2003) d Classes 322-324 and 333 are assigned a K y factor equal to 0·5 to denote their drought tolerance enabling eco-physiological responses (Baquedano & Castillo, 2007;Ozturk et al, 2010;Padilla & Pugnaire, 2007). Coumou et al, 2013;Russo et al, 2014;Lehner et al, 2016).…”

Section: Future Periodssupporting

confidence: 74%

“…This cropwater production function has shown a remarkable validity at basin scale, field scale (Yacoubi et al, 2010), and in decision support systems (Gastélum et al, 2009) and can be applied to all agricultural crops, that is, herbaceous, trees and vines. K y can be estimated either experimentally using the Doorenbos & Kassam (1979) formula for crops under conditions of different irrigation practices and lack of water at different growth stages (Kipkorir et al, 2002;Moutonnet, 2002;Akhter et al, 2003;Lovelli et al, 2007;Istanbulluoglu, 2009;Kuslu et al, 2010;Steduto et al, 2012), or using existing databases for crop yield response factors which are verified in the field (Popova et al, 2006). Upon estimation, K y is an agent for quantification of the resistance of crops to water stress (Lovelli et al, 2007;Singh et al, 2010) as shown in Table I.…”

Section: Methodsmentioning

confidence: 99%

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Yield Response of Mediterranean Rangelands under a Changing Climate

et al. 2017

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Understanding the Mediterranean rangelands degradation trends is a key element of mitigating their vulnerability and enhancing their resilience. Climate change and its inherent effects on mean temperature and the precipitation variability can regulate the magnitude, frequency and duration of droughts and aridity with a profound effect on ecosystem productivity. Here we investigate the effects of climate change to project the development of vegetation in the Mediterranean rangelands by (i) estimating the relative Standardized Precipitation Index and a modification of the United Nations Environment Programme Aridity Index to classify climate variability, and (ii) modelling vegetation response to climate using the Food and Agriculture Organisation crop–water production function. Climate model data are obtained from nine general circulation models under Relative Concentration Pathways 2.6 and 8.5 of the fifth phase of the Coupled Model Intercomparison Project. After correcting climate model data for biases, results for two 40‐year future study periods are compared with the baseline period 1961–2000 within a domain that includes the European Mediterranean. We show that a gradual but robust increase of aridity and drought frequency is estimated for most of the Mediterranean region, impacting rangeland vegetation yields. Projected drought and aridity disturbances may well represent permanent shifts to a warmer and more frequently dry status. This alternative stability of climatic pressure lies outside the limits of ecosystem resilience and may indicate that in some cases vegetation will either adapt to the new conditions or be succeeded by more water‐stress tolerant species. Results raise concerns about the fate of the Mediterranean rangelands and the effectiveness of mitigation measures. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Future Periodssupporting

confidence: 74%

Section: Methodsmentioning

confidence: 99%

Yield Response of Mediterranean Rangelands under a Changing Climate

et al. 2017

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“…had a WUE value of 4.55 mg per g in a greenhouse study [38]. Likewise, seedlings of Arabian drop-seed grass (Sporobolus arabicus) and bearded sprangletop (Leptochloa fusca), when harvested after reaching maximum biomass, had WUE values of 1.0 to 1.4 mg g −1 [39]. Switchgrass WUE in the field in Nebraska had values of 1.0 to 5.5 mg per g [40], similar to switchgrass seedlings in a growth chamber (1.45 to 5.5 mg per g) [41]).…”

Section: Calculating Wue With the Almanac Modelmentioning

confidence: 97%

A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

Kiniry

Kim²

2020

Agronomy

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Whole plant productivity is obviously the ultimate product of leaf photosynthesis and this has led to numerous efforts to relate the two. However, often with perennial grasses, plant productivity is more sink-limited than source-limited, causing the linkage between the photosynthetic rate and productivity to be weak or nonexistent. This has led to a different approach, characterizing plant productivity in terms of the efficiency of intercepted light use in producing biomass, also called radiation use efficiency. Likewise, the efficiency of the use of water to produce plant biomass, or water use efficiency, has been the object of much interest. The use of a simulation model to quantify biomass, using radiation use efficiency in parallel with a daily water balance simulation, allows for the effective calculation of water use efficiency. In this project, the process of determining radiation use efficiency with field data is described, as well as example values for highly productive perennial grasses useful for feedstock for bioenergy. In addition, values of water use efficiency for these grasses are reported and compared with other perennial grasses and common cultivated crops.Water requirements for growing switchgrass and giant miscanthus are often an issue. Without adequate water, their biomass yields can be reduced by 45%-80% of total biomass yields [10,11]. Moreover, competition for water with other users makes maximizing water use efficiency vitally important. Again, process-based simulation models are valuable tools since they simulate the dynamics of crop water use based on evaporation of soil water, leaf transpiration, weather, and dynamics of plant communities. Quantifying Photosynthetic Performance via Two Approaches: Single Leaf Photosynthesis vs. Radiation Use Efficiency (RUE)There are many plant simulation models based on single leaf photosynthesis, which is then scaled up to the whole leaf canopy. Such models, such as ORYZA for rice (Oryza sativa L.) [12,13], are based on the assumption that photosynthesis is a major driver and determinant for whole plant productivity. However, frequently grass systems have been shown to be more sink-limited than source-limited. They often rely more on the processes of tiller production, leaf development, and leaf canopy orientation.Examples:

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“…where T [ • C] is the mean daily temperature and ξ is the mean daily percentage of annual daytime hours. The crop coefficient K c can be estimated experimentally [38][39][40][41][42][43][44] or found in crop yield response factor datasets which are verified in the field [45]. For example, for olive trees, evapotranspiration (or irrigation), requirements are considered from March to November (Day of year 90-330) and, depending on agricultural management or crop type and phenology, may take different values, as shown in Figure 1.…”

Section: Crop Yieldmentioning

confidence: 99%

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

et al. 2020

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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.

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Untitled

Cited by 14 publications

References 33 publications

Yield Response of Mediterranean Rangelands under a Changing Climate

Yield Response of Mediterranean Rangelands under a Changing Climate

A Review of Modeled Water Use Efficiency of Highly Productive Perennial Grasses Useful for Bioenergy

Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support

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