Spatio-temporal variability of dry and wet spells and their influence on crop yields

Gobin, Anne; Vyver, Hans Van de

doi:10.1016/j.agrformet.2021.108565

Cited by 17 publications

(18 citation statements)

References 48 publications

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“…The agricultural regions differ distinctly in their agro-ecological conditions as characterised by their location, topography and dominant soil reference groups (Table 3). These characteristics related well to climate extremes and arable crop performances (e.g., Gobin and Van de Vyver, 2021;Zamani et al, 2016). Mean changes of the indicators per grid point and per agricultural region were compared for the periods 1971-2018 and 2019-2068.…”

Section: Spatio-temporal Analysismentioning

confidence: 99%

Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068

Drepper

Gobin

Orshoven

2022

Agricultural and Forest Meteorology

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Section: Spatio-temporal Analysismentioning

confidence: 99%

Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068

Drepper

Gobin

Orshoven

2022

Agricultural and Forest Meteorology

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“…The occurrence of meteorological events, such as frost or drought, during the crop life cycle or at specific phenological stages explains yield variation in common crops [7][8][9][10]. Crop phenology and canopy development help improve the effectiveness of management practices and the accuracy of decision support systems [11,12].…”

Section: Introductionmentioning

confidence: 99%

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

et al. 2023

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Understanding crop phenology is crucial for predicting crop yields and identifying potential risks to food security. The objective was to investigate the effectiveness of satellite sensor data, compared to field observations and proximal sensing, in detecting crop phenological stages. Time series data from 122 winter wheat, 99 silage maize, and 77 late potato fields were analyzed during 2015–2017. The spectral signals derived from Digital Hemispherical Photographs (DHP), Disaster Monitoring Constellation (DMC), and Sentinel-2 (S2) were crop-specific and sensor-independent. Models fitted to sensor-derived fAPAR (fraction of absorbed photosynthetically active radiation) demonstrated a higher goodness of fit as compared to fCover (fraction of vegetation cover), with the best model fits obtained for maize, followed by wheat and potato. S2-derived fAPAR showed decreasing variability as the growing season progressed. The use of a double sigmoid model fit allowed defining inflection points corresponding to stem elongation (upward sigmoid) and senescence (downward sigmoid), while the upward endpoint corresponded to canopy closure and the maximum values to flowering and fruit development. Furthermore, increasing the frequency of sensor revisits is beneficial for detecting short-duration crop phenological stages. The results have implications for data assimilation to improve crop yield forecasting and agri-environmental modeling.

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“…Prolong periods of dry spell facilitate favorable conditions for drought development – influencing heat waves (Ganguli 2022 ), livestock-based economy (Sørensen et al 1993 ), growth stages of rainfed-crops (Gobin 2018 ), irrigation scheduling (Daccache et al 2015 ), frequency of forest fires (van Bellen et al 2010 ; Flannigan et al 2000 ) and global water resources (Hettiarachchi et al 2022 ). However, long periods of wet spells develop waterlogging conditions in field crops (Gobin and Van de Vyver 2021 ), floods at regional and catchment scale (Blöschl et al 2017 ; Kundzewicz et al 2013 ; Zolina 2014 ), prevalence of vector-borne diseases (Segun et al 2020 ) and landslides in mountainous regions (Das and Ganguli 2022 ). Changes in dry and wet spells properties influences water quality (Benotti et al 2010 ; van Vliet and Zwolsman 2008 ; Whitworth et al 2012 ), urban water management (Chowdhury and Beecham 2013 ) and hydrologic extremes (Breinl et al 2020 ).…”

Section: Introductionmentioning

confidence: 99%

On the Structure of the Intermittency of Rainfall

Dey

2023

Water Resour Manage

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Quantification of rainfall intermittency via. interevent time distribution, series of continuous wet spells (burst size) and variability in interevent times between rainfall events is essential for planning and management of water resources and hydrologic extremes. However, their structure, quantification and association with long-term climatology are less explored. In this paper, a complex system-based measure – burstiness – is used to quantify the variability of interevent times across six meteorologically homogenous zones of India. It is observed that burstiness is related to the burst size as well as long-term rainfall climatology. The existence of unimodal and bimodal structure in burstiness distribution reveals the uniqueness and differences in the rainfall patterns. The differences in sensitivity of rainfall to burstiness highlight the role of interplay between climate-landscape and reveals the importance of the intermittent structure of rainfall. The study provides an approach to model intermittency by preserving the temporal structure of the interevent time distribution.

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Spatio-temporal variability of dry and wet spells and their influence on crop yields

Cited by 17 publications

References 48 publications

Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068

Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068

Crop Phenology Modelling Using Proximal and Satellite Sensor Data

On the Structure of the Intermittency of Rainfall

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