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Record W2800577614 · doi:10.5339/qfarc.2018.eepp126

Improving Vegetable Crop Production in Qatar: Strategies to determine optimum planting time minimise production risk and maximise water and nutrient use efficiency

2018· article· en· W2800577614 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueQatar Foundation Annual Research Conference Proceedings Volume 2018 Issue 1 · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWater management and technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureFood securityBusinessArable landFood processingAgricultural productivityProduction (economics)Sustainable agricultureAgricultural economicsAgricultural engineeringEnvironmental planningEnvironmental scienceEngineeringGeographyEconomics

Abstract

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Introduction The research developed strategies to be adopted in Qatar to enhance agricultural production and improve the food security situation of the country. The government of Qatar has instituted plans to boost its food security and, as part of the wider food security plan, the country needs improved agricultural technologies and in-country farming techniques to increase crop yields. This QNRF-funded food security project sought to apply innovative technologies and decision support tools to provide strategies for successful vegetable food production on the local Qatari scene. The research has provided methodologies to assist Qatari researchers and Policy makers to make informed decisions about appropriate crops and timing for profitable in-country farming. Rationale The agricultural production problems faced by Qatar are similar to many other arid and semi-arid countries. The challenges faced by Qatar in improving agricultural production represent a wide range of sustainable agriculture issues including limited arable land and water resources, high temperatures associated with high humidity, salinity, plant pests and plant diseases. This research addresses these problems by contributing to the application of innovative ways of maximizing water and nutrient use efficiency for agriculture, and choosing the appropriate planting times for vegetable cultivation. Appropriate preservation of soils for agricultural purposes is not practiced in Qatar. Therefore, this research was carried out to showcase modern technological innovations with the potential to accelerate in-country vegetable production so as to enhance food security in Qatar. Methods The project consisted of four sub-projects, namely Sub-Project 1 (crop management), Sub-Project 2 (soil management and desertification), Sub-Project 3 (financial risk management) and Sub-Project 4 (combination of the three sub-Projects). The respective tasks of the sub-Projects were as follows Sub-Project 1 developed a decision-making framework for sustainable and profitable cropping in Qatar, and used the FAO AquaCrop model to carry out crop growth simulations of cucumber, squash, and tomato (Qatar), and wheat (Australia). Sub-Project 2 utilised the Soil Constraints and Management Package (SCAMP) to assess intrinsic soil and site constraints for sustainable production. The sub-Project also identified several strategies to improve crop productivity. Sub-project 3 derived an integrated drought derivative model that incorporated geo-specific weather factors, crop-growth cycles, and soil management practices to minimize financial risk. Sub-Project 4 combined the climate, crop, soil and financial components to develop an integrated decision-making framework as well as a targeted partner engagement and communication strategy. Results and Discussion The AquaCrop model is recommended for use in simulating yields of crops to assess the effects of agronomic practices including planting dates, soil characteristics, and water/nutrient use efficiency. Months for optimum vegetable planting are September and December. March planting can produce similar yields but more water is required. Because of higher temperatures and greater vapour pressure deficits during the March to June period, field crop yields from these planting dates are highly variable, resulting in very inefficient use of water. It is therefore recommended that planting during this time period in the field should be avoided. It is critically important to maximize water use efficiency in irrigated field crops in Qatar because of the link between water use efficiency and nitrogen use efficiency. It is essential that N fertilizer inputs closely match crop N requirements, and ammonium-based N fertilizers be used in preference to nitrate-N fertilizers. Regular monitoring of the salinity of the irrigation water is required to ensure that it does not exacerbate the current soil salinity status. As nutrient budgets of field-grown squash and tomato have identified a mismatch between nutrient inputs and crop demand in current management practices, the key soil fertility analyses of extractable phosphorus, organic carbon and exchangeable potassium should be undertaken to inform fertilizer management.The Financial Drought model developed using the reconnaissance drought index (RDI) can be used. A policy brief is available on the operation of derivative systems to protect farmers from production risk.The optimization model developed by incorporating a risk measure is recommended for further application. The developed user-friendly output interface for the model can be used for effective application. Significance The project initiated new dialogue between Government and private sector stakeholders in Qatar on food security strategies. Stakeholders were made aware of the lack of current information on the fertility of soils and quality of irrigation water used for vegetable crops in Qatar. The project concentrated on field production of vegetable crops (cucumber, squash and tomato) identified by a preliminary survey of local consumers. Training opportunities were provided for six students in Qatar, Canada and Australia. Results were presented at eight international conferences where QNRF was acknowledged. An analysis of evapotranspiration data for Qatar was conducted and results published. This is the first study of its type for Qatar (Issaka et al. 2017). Conclusions The research has developed a strategy that is in alignment with QNRS Grand Challenge # 1 - Water Security, by focusing on water use efficiency for crop production. It also informs on the optimal times for planting vegetable crops, and reduces fertilizer inputs and costs by avoiding excessive fertilizer use. A key component of the strategy is the need for fertilizer inputs to be closely matched to crop nutrient requirements using a nutrient budget and soil test approach. Regular monitoring of irrigation water salinity is essential to avoid increased salinization. The Financial Drought model will be beneficial in protecting farmers from production risk. Finally, the optimization model will be beneficial in improving farming decisions. Recommendations for Future Research and Development There are major advances in water and nutrient use efficiency that could be applied in both open field and greenhouse agriculture in Qatar, with the objective of making maximum effective use of their very limited water and arable soil resources. Examine how project outputs (AquaCrop/economic and crop selection model, soil / water/ nutrient/ solar radiation use efficiency, communication packages) can be embedded in Qatar's Research and Educational programs. Engage more actively with the Department of Agriculture of the Ministry of Municipality and Environment, and its particular interests in both the broader food security issues, and the tools being offered by the project. Acknowledgement This research was made possible by a NPRP award [NPRP 6-064-4-001] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements in the Report are solely the responsibility of the authors. References A.K.S. Huda, A.I. Issaka, S. Kaitibie, M. M. Haq, I. Goktepe, A. Moustafa, K. Abdella, M. Pollanen, P.W. Moody, N. Vock, N. Huda, and K.J. Coughlan (2017). Improving Food Security in Qatar: Assessing Alternative Cropping Systems Feasibility and Productivity in Variable Climates, Soil and Marketing Environments (NPRP 6-064-4-001). Final Report, Qatar National Research Foundation, 79pp A. I. Issaka, J. Paek, K. Abdella, M. Pollanen, A. Huda, S. Kaitibie, I. Goktepe, M. Haq and A. Moustafa (2017). «Analysis and Calibration of Empirical Relationships for Estimating Evapotranspiration in Qatar: Case Study.» Journal of irrigation and drainage engineering 143(2): 05016013.

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Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.282
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.278
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it