Abstract
Climate change has devastating impacts on agriculture, increasing the yield gap for most crops, especially in developing nations. This is likely to worsen food insecurity in some countries, calling for efforts to close the yield gap as much as possible. Estimating the yield gap and its drivers is essential for devising strategies to increase yields. This study quantifies the wheat yield gap in Morocco's five major wheat production regions. It analyzes the historical sensitivity of wheat yield to temperature, precipitation, and soil moisture, which are important factors affecting agricultural productivity. Furthermore, it evaluates how these yield gaps impact the revenue of producers in these regions. This analysis was conducted using datasets, including the Global Dataset of Historical Yield (GDHY) for yield gap assessment, soil moisture data, ERA5 reanalysis data, and CHIRPS datasets for climate assessment from 1982 to 2016. Pearson correlation and multiple linear regression analyses were employed to reflect the variation characteristics of wheat yield and to identify the impacts of precipitation, temperature, and soil moisture on wheat yield. High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%. Wheat yields were found to be strongly correlated with rainfall, particularly from December to March. Temperature fluctuations had a significant negative impact on wheat yield across the regions. Soil moisture was positively correlated with wheat yields throughout all growing periods, showing the strongest impacts during the early vegetative development phase. Additionally, losses due to wheat yield gaps were considerable, ranging between $ 194 and 891 per hectare. The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap. Results reveal gaps in wheat yield, forming a basis for process-based modeling to understand crop yield gap drivers. Understanding yield gap drivers will play a pivotal role in evidence-based intervention strategies to enhance yields. By applying such strategies, it is possible to not only manage and reduce the variability in wheat production, but also ensure sustainable agricultural practices and achieve food security in Morocco and beyond.
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Claims 8
Claim · model-written summary
Wheat yield gaps varied regionally from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi.
- Taxon
- wheat
Triticum aestivume0 - Outcome
- wheat yield gapse0
- Value
- 1.64 t/hae0
- Qualifier
- 4.12 t/ha in Marrakech Safie0
- Setting
- Casablanca Settate0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%.”
Claim · model-written summary
Wheat yield gaps showed temporal variability of 9 to 18%.
- Taxon
- wheatTriticum aestivume0
- Outcome
- temporal variabilitye0
- Value
- 9 to 18%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%.”
Claim · model-written summary
Wheat yields correlated strongly with rainfall, especially December to March.
- Taxon
- WheatTriticum aestivumt0
- Treatment
- rainfalle0
- Outcome
- Wheat yieldse0
- Qualifier
- December to Marche0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Wheat yields were found to be strongly correlated with rainfall, particularly from December to March.”
t0“wheat yield gap”
Claim · model-written summary
Temperature fluctuations had a significant negative impact on wheat yield.
- Taxon
- wheatTriticum aestivume0
- Treatment
- Temperature fluctuationse0
- Outcome
- wheat yielde0
- Direction
- negativee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Temperature fluctuations had a significant negative impact on wheat yield across the regions.”
Claim · model-written summary
Soil moisture was positively correlated with wheat yields throughout all growing periods, strongest early in vegetative development.
- Taxon
- wheatTriticum aestivume0
- Organ / stage
- early vegetative development phasee0
- Treatment
- Soil moisturee0
- Outcome
- wheat yieldse0
- Direction
- positivelye0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Soil moisture was positively correlated with wheat yields throughout all growing periods, showing the strongest impacts during the early vegetative development phase.”
Claim · model-written summary
Losses from wheat yield gaps ranged between $ 194 and 891 per hectare.
- Taxon
- wheatTriticum aestivume0
- Outcome
- losses due to wheat yield gapse0
- Value
- $ 194 and 891 per hectaree0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Additionally, losses due to wheat yield gaps were considerable, ranging between $ 194 and 891 per hectare.”
Claim · model-written summary
Revenue loss due to Yield Gap I ranged from 49 to 71%.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- revenue loss due to Yield Gap Ie0
- Value
- 49 to 71%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap.”
t0“wheat yield gap”
Claim · model-written summary
Revenue loss due to Yield Gap II ranged from 240 to 444%.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- loss due to Yield Gap IIe0
- Value
- 240 to 444%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The revenue loss due to Yield Gap I ranged from 49 to 71%, while the loss due to Yield Gap II ranged from 240 to 444%, depending on the method used to calculate the wheat yield gap.”
t0“wheat yield gap”