Dynamic modeling of cucumber crop growth and uptake of N, P and K under greenhouse conditions

Ramírez-Pérez, Lino J.; Morales-Díaz, América Berenice; Benavides-Mendoza, Adalberto; de-Alba-Romenus, Karim; González-Morales, Susana; Juárez‐Maldonado, Antonio

doi:10.1016/j.scienta.2018.02.068

Cited by 31 publications

(13 citation statements)

References 37 publications

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“…Hydrolysis Product Enzymatic (alfalfa hay, pulses, and vegetable or fruit waste) and chemical (feathers, bone meal, casein, collagen from skins, animal tissue, or fish waste) Increase in yield [32] Increase in nitrogen and phosphorus content in leaves and macro-and micronutrients [33,34] Increase in protein content in cereal grains [33] Protection against biotic and abiotic stresses [35] Increased soil fertility through the development of soil microorganisms [36] Anaerobic Digestion Product Plant, animal, and lignin biomass Call of the auxin-like effect [23,37] Improving the availability of nutrients [31] Biopreparations from Marine Algae Ascophyllum nodosum, Sargassum wightii, Ecklonia maxima, Enteromorpha intestinalis, Gelidium pectinutum…”

Section: Source Of Biostimulant Example Main Activitymentioning

confidence: 99%

Plant Biostimulants: Importance of the Quality and Yield of Horticultural Crops and the Improvement of Plant Tolerance to Abiotic Stress—A Review

2019

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Biostimulants are among the natural preparations that improve the general health, vitality, and growth of plants and protect them against infections. They can be successfully used in both agri- and horticultural crops. The main active substances used in such preparations are humic and fulvic acids, protein hydrolysates, compounds containing nitrogen, seaweed extracts, beneficial fungi, and bacteria. Biostimulant formulations may be single- or multi-component, but the synergic action of several different components has been observed. Many groups of biostimulants have been distinguished through their method of application (soil, foliar), the material from which they were produced (plant, animal), or the process by which they were created (hydrolysis, fermentation, extraction). Natural soil stimulants can induce the development of beneficial soil organisms that provide substrates for plant growth. The use of natural preparations that are not harmful to the environment is particularly important in connection with the progressive processes of soil degradation and atmospheric pollution. This review gives an overview of the importance and influence of different natural plant biostimulants on both the yield and quality of crops.

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Section: Source Of Biostimulant Example Main Activitymentioning

confidence: 99%

Plant Biostimulants: Importance of the Quality and Yield of Horticultural Crops and the Improvement of Plant Tolerance to Abiotic Stress—A Review

2019

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“…Crop modelling has been gaining momentum over the past decade; partly due to the necessity of sustainable agriculture under environmental degradation along with population growth (Overman and Martin, 2001;Overman et al, 2003;Sepaskhah et al 2011;Du et al, 2014;Camargo et al, 2015;Ramírez-Pérez et al, 2018). Such models help to assess the most appropriate planning in agro-environmental systems, through optimization of water and fertilizer application and finally maximizing returns to farmers.…”

Section: Introductionmentioning

confidence: 99%

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Rafiee

Mahbod

2020

WSA

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Eggplant is an important product in greenhouse cultivation in the world. However, much of its production is done on the farm. Few studies have been conducted on modelling of its growth and yield. Considering the simplicity of the logistic models, only the temperature and dry matter data during the growing season are needed to calibrate them. Hence, we evaluated the performance of the logistic model in growth and yield prediction of eggplant in greenhouse and farm conditions. Eggplants were planted in 2012 and 2013 under different treatments of irrigation and salinity with a complete randomized block statistical design. Irrigation frequency treatments consisted of: daily (I1), weekly, (I2) and every 2 weeks of irrigation (I3). Each pot was irrigated to field capacity level. Four levels of salinity treatments are as follows: electrical conductivities (EC) of 0.8 (J1); 2.5 (J2); 5.0 (J3) and 7.0 (J4) dS∙m-1. The amount of plant dry matter was measured during the growing season (DM) and the amount of product (Y) at the end of the growing season. The logistic model was calibrated with the first-year data and validated with the second-year data. Logistic equation coefficients and harvest index were estimated as a function of the depth and electrical conductivity of irrigation water. The results showed that the accuracy of the logistic model for estimating DM during the growing season was good and predicted the product at the end of the growing season with acceptable accuracy. Also, the model's agreement with the measured DM and Y was good.

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“…To address these problems, a viable opportunity in crop modelling tools by classifying potential crop yields and possible crop plantation limitations of specific zones has been designed (Droogers and Hunink, ). In this regard, mathematical modelling is an efficient and convenient tool for increasing crop production and farm management under inadequate water resource situations for researchers and managers (Prathumchai et al, ; Ramírez‐Pérez et al, ). Crop modelling is an alternative means of promoting efficient irrigation usage in the region.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Aquacrop Model in Simulating Bitter Gourd Water Productivity Under Saline Irrigation

Soomro

Alaghmand

Shahid

et al. 2019

Irrigation and Drainage

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The agricultural sector, globally, faces multifaceted challenges to produce more with a minimum amount of water to enhance water productivity (WP). In this regard, crop modelling is an efficient approach for estimating WP and plays a significant role in addressing water management strategies. The present research has been conducted in Malir, Karachi—a semi‐arid region in the Sindh Province of Pakistan. The aim was to evaluate the effects of two different qualities of irrigation water including, fresh quality (IT1 0.56 dS m−1) and saline groundwater (IT2 2.89 dS m−1) on canopy cover (CC), biomass, crop yield and water productivity (WP) of bitter‐gourd. The AquaCrop model was used for the first time for bitter gourd in a semi‐arid region under drip irrigation. Simulations were carried out for two cropping seasons (i.e. season one (summer) and season two (winter). The modelled values were slightly higher than the measured biomass and yield values in both calibration and validation procedures. The performance of the AquaCrop model was estimated through different statistical indicators such as model efficiency (E), root mean square error (RMSE), and index of agreement (dindex). These indicators revealed that the model simulated bitter gourd biomass and crop yield well. In addition, the model's performance was good under all irrigation treatments, with RMSE in the range 1.2–3.7, 0.5–1.8, dindex 0.94–0.98 and E 0.93–0.97. The model also overestimated the biomass and crop yield. Overall, the AquaCrop model overestimated biomass and crop yield for both seasons. © 2019 John Wiley & Sons, Ltd.

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Dynamic modeling of cucumber crop growth and uptake of N, P and K under greenhouse conditions

Cited by 31 publications

References 37 publications

Plant Biostimulants: Importance of the Quality and Yield of Horticultural Crops and the Improvement of Plant Tolerance to Abiotic Stress—A Review

Plant Biostimulants: Importance of the Quality and Yield of Horticultural Crops and the Improvement of Plant Tolerance to Abiotic Stress—A Review

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Evaluation of Aquacrop Model in Simulating Bitter Gourd Water Productivity Under Saline Irrigation

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