Adaptation strategies to climate change using cotton ( Gossypium hirsutum L.) ideotypes in rainfed tropical cropping systems in Sub-Saharan Africa. A modeling approach

Gérardeaux, Edward; Loison, Romain; Palai, Oumarou; Sultan, Benjamin

doi:10.1016/j.fcr.2018.07.007

Cited by 14 publications

(9 citation statements)

References 39 publications

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“…In addition, when the amount of rainfall was beyond the minimum value (near 591 mm in Figure 6c), there was a positive correlation between cumulated rainfall and cotton yield. This contradicts [9], who, in a recent study, reported that there is a pronounced decrease in production above 700 mm using cottonseed yields simulated by the generic CROP GROwth model (CROPGRO). These differences may be due to the disparities in the simulation and time scales used.…”

Section: Critical Points For Cotton Yieldmentioning

confidence: 85%

“…However, the analysis also showed that an onset date, rainfall intensity, heavy rainfall, CDD, Rx1day and R20mm of 127, 12.5 mm•day −1 , 405 mm, 27 days, 67 mm and 22 days, respectively, were depicted for optimum cotton yield. Recent modeling studies predict that increasing temperatures and the fertilizing effects of CO 2 will shorten crop cycles with no negative effect and increase yields, respectively [9]. The area featuring high and very high agricultural drought hazards are distributed in most parts of the study domain [10].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have demonstrated the influence of rainfall seasonal parameters, such as the rainy season length, the number of dry spells, the flooding periods, temperature and solar radiation. These studies are based on observed data analyses and models' estimates in experimental stations and on-farm situations about cotton productivity [6][7][8][9][10] and the vegetative yield in the Sahel [11] and North Cameroon [12] at regional and local Since the 1950s, North Cameroon, with its Sudano-Sahelian climate, has been explicitly chosen to create an optimal representation of cotton cultivation after many experiments. [4] have shown that the 1980s and the early 2010s were characterized by severe drought.…”

Section: Introductionmentioning

confidence: 99%

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Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

et al. 2021

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Cotton yields in the Sudano-Sahelian region contribute to food security through their role in agricultural productivity. Daily precipitation data and cotton yield data were synthesized from nine agricultural regions obtained from the “Société de Développement du Coton (SODECOTON)”. The following seasonal rainfall indices—from Cameroon’s cotton zone—were mapped with geographic information systems for spatial analysis: wet season onset and retreat date, rainfall amount, number of rainy days, rainfall intensity (SDII), heavy-rainfall events (R95p), consecutive dry days (CDD), annual highest daily precipitation (Rx1day) and number of very heavy precipitation days (R20mm). Linear regressions were used as statistical tools for analysis. The strongest relationships were observed between cotton yields and the heavy-rainfall events, closely followed by seasonal rainfall amount. An increase in consecutive dry days (CDD) and heavy events, and a decreased seasonal rainfall amount, have a negative impact on cotton yield trends. Overall, the critical breakpoint analysis between cotton yields and all rainfall indices showed that the cotton yield was particularly negatively impacted before a 251 retreat date, 591 mm seasonal rainfall amount and 33 rainy days. By contrast, an onset date, rainfall intensity, heavy rainfall, CDD, Rx1day and R20mm of 127, 12.5 mm·day−1, 405 mm, 27 days, 67 mm and 22 days, respectively, were identified for an optimum cotton yield. These results can be used as information for agricultural activity and management, civil planning of economic activities and can also contribute to furthering our understanding of the management impacts on future food security.

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Section: Critical Points For Cotton Yieldmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

et al. 2021

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“…Africa is another world region where it is important to promote cleaner farming systems [52], in a framework of CSA practices [155], and where several projects [156], and studies were carried out [157]. This considers the complexity of the African farming systems [158] and its vulnerability to the global warming [159]. The impacts of the climate change in the African countries are, in fact, problematic [160].…”

Section: References Particularities and Solutionsmentioning

confidence: 99%

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Martinho

Guiné

2021

Agronomy

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The innovative technologies developed in the different fields of science (nanotechnology, artificial intelligence, genetic modification, etc.) opened new and infinite possibilities for the several stakeholders that carry out their activities in the different economic sectors. For agriculture, these new approaches are particularly relevant and may bring interesting contributions, considering the specificities of the sector, often dealing with contexts of land abandonment and narrow profit margins. Nonetheless, the question in these unstopped evolutions is about the interlinkages with sustainability. In this context, the objectives of this study are to highlight the main insights from the available scientific literature about the interrelationships between the new trends in the agriculture and the sustainability. To achieve these aims, a search on the Web of Science Core Collection (WoS) and Scopus databases was carried out, on 15 May 2021, for the topics ‘smart agriculture’ and ‘sustainability’. A total of 231 documents (102 from WoS and 129 from Scopus) were obtained, remaining 155 documents after removing the duplicated, which were surveyed through systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. As main insights, the concerns of the researchers with the impacts on the sustainability from the transformations in the farming organization are highlighted. On the other hand, it was shown the relevance and the new opportunities, including in terms of food supply, arising from the precision agriculture, agricultural intelligence, vertical/urban farming, circular economy, internet of things, and crowdfarming. We suggest the new and wider concept of ‘integrated-smart agriculture’, better than ‘climate-smart agriculture’.

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“…Most importantly, the FAO-56 ET method (Allen et al, 1998), which was used by Adhikari et al (2016), calculated consistently lower seed-cotton yields for the sequential project and hence Priestly-Taylor method was used in this study. A majority of other DSSAT cotton modeling studies in the literature have also used the Priestley-Taylor method (e.g., Guerra et al, 2007;Garcia et al, 2008;Gérardeaux et al, 2018). The SOM simulation method was also changed from the CERES (Godwin and Singh, 1998) to the CENTURY (Gijsman et al, 2002) method as the latter method simulates soil carbon and nitrogen dynamics more efficiently than the former method.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

Ale¹,

Himanshu²,

Mauget³

et al. 2021

Front. Sustain. Food Syst.

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In the Texas High Plains (THP), diminishing irrigation well-capacities, and increasing costs of energy and equipment associated with groundwater extraction and application are contributing factors to a transition from irrigated to dryland agriculture. The primary goal of this modeling exercise was to investigate whether and to what extent hypothetical changes in factors putatively associated with soil health would affect dryland cotton (Gossypium hirsutum L.) yields. The factors selected were drainage, surface runoff, soil water holding capacity, soil organic carbon (SOC) and albedo. As a first analysis to evaluate these factors, we used the CROPGRO-Cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model. Specifically, we evaluated the effects of reduced surface runoff, increased soil water holding capacity, and SOC, doubling of the soil albedo through stubble mulching, and of soil drainage by enhancing infiltration with no-tillage/cover crops on yield by adjusting related soil properties. In our analysis, we used mean yields simulated with soil properties of a Pullman clay loam soil at Halfway, TX on the THP as baseline, which were compared to values obtained with the adjusted factors using weather data from 2005 to 2019. Simulated mean yield increased by 27% when the soil water holding capacity was increased by 25 mm, 7% when the runoff curve number was decreased from 73 to 60, 16% when soil albedo was increased from 0.2 to 0.4, and by 58% when the soil drainage factor (fraction day−1) was doubled from 0.2. No significant statistical change in simulated mean yield was calculated when SOC was increased by 1%. Further, effects of a 50 mm pre-plant irrigation were also assessed, simulating limited irrigation in the transition to dryland agriculture that resulted in a statistically insignificant 12% increase in seed-cotton yield. Simultaneous implementation of the four statistically significant individual scenarios (increased water holding capacity, infiltration, albedo, and drainage) resulted in the highest increase (93%) in mean seed-cotton yield. An economic and risk analysis of simulated yields under different scenarios indicated that these factors could reduce revenue risk for dryland cotton producers, with most of the effect from soil drainage improvements.

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Adaptation strategies to climate change using cotton ( Gossypium hirsutum L.) ideotypes in rainfed tropical cropping systems in Sub-Saharan Africa. A modeling approach

Cited by 14 publications

References 39 publications

Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

Contributions of Seasonal Rainfall to Recent Trends in Cameroon’s Cotton Yields

Integrated-Smart Agriculture: Contexts and Assumptions for a Broader Concept

Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

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