Spatiotemporal analysis of rainfed cereal yields across the eastern high plateaus of Algeria: an exploratory investigation of the effects of weather factors

Benmehaia, Mohamed Amine; Merniz, Noureddine; Oulmane, Amine

doi:10.1007/s41207-020-00191-x

Cited by 6 publications

(4 citation statements)

References 40 publications

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“…Moreover, they work daily on their land and have a deep knowledge of their groves. Furthermore, their experience allows them to become environmental experts if they are taught or well advised (Benites et al 2005;Kosmas et al 2016;Sastre et al 2016), meaning that farmers can advise on land management and environmentally friendly policies. This is something that seems to have recently been discovered by politicians and scientists.…”

Section: Introductionmentioning

confidence: 99%

Long-term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain)

Rodrigo‐Comino

Senciales-González

et al. 2021

Euro-Mediterr J Environ Integr

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Economic, social, and climatic conditions affect agricultural production. Those changes are relevant to the rainfed agricultural areas of the Mediterranean Belt, including Spain—the largest producer of olive oil in the world. However, little is known about the effect of the climate on olive production and farmer income. In this study, the correlation between changes in rainfall and total olive production was examined using a long-term dataset (28 years) on conventional rainfed production and tillage soil management. The dataset focused on different olive groves in the municipality of Bailén (Jaén, Andalusia) that have been owned by the same farmer since 1966. The province of Jaén is the region of Spain with the highest production of olive oil and the largest area of olive groves. The data included annual rainfall, production per plot and the price of olives. After calculating missing data to complete the rainfall series, pairwise correlation analysis with nonparametric Spearman's rank coefficients and principal component analysis were used to process the data. The results showed that higher production coincided with increased rainfall during August and December. Therefore, we concluded that the impact of rainfall on olive production is variable and depends on drought intensity and the monthly rainfall distribution. An economic study showed that farmer income was highly dependent on the seasonal distribution of the rainfall among other factors such as the price of olives. Farmer income was low during drought periods, indicating that rainfed agriculture is perceived by farmers as unsustainable due to the resulting highly variable income. This study could help to prevent risks to food security in the future. We recognise that other key factors have also been important influences on the fluctuations in olive production over the years, such as soil properties and plant status. However, cultivating olives without irrigation—depending only on the total rainfall amount and rainfall intensity to supply all of the water consumed by the plants—is very risky too. This research demonstrates that the subsistence of Mediterranean rainfed olive farmers can be highly dependent on the rainfall conditions.

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Section: Introductionmentioning

confidence: 99%

Long-term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain)

Rodrigo‐Comino

Senciales-González

et al. 2021

Euro-Mediterr J Environ Integr

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“…ence to recent few studies such as: Belaidi, 2013;Benmihoub et al, 2016;Akli et al, 2019;Belaidi et al, 2019;Oulmane et al, 2020).…”

Section: Introductionmentioning

confidence: 79%

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2022

New Medit

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“…The study area was located in Algeria, a cereal producer facing high inter-annual climatic variability (Benmehaia et al, 2020 ). Our analysis focussed on more than 20 provinces (Fig.…”

Section: Methodsmentioning

confidence: 99%

Is deeper always better? Evaluating deep learning models for yield forecasting with small data

Sabo,

Meroni,

Waldner

et al. 2023

Environ Monit Assess

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Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements—public and global records of satellite data in an application ready format with near real time updates—and can be transferred to any country with reliable yield statistics. Three-dimensional histograms of normalized difference vegetation index (NDVI) and climate data are used as input to the 2D model, while simple administrative-level time series averages of NDVI and climate data to the 1D model. The best model architecture is automatically identified during efficient and extensive hyperparameter optimization. To demonstrate the relevance of this approach, we hindcast (2002–2018) the yields of Algeria’s three main crops (barley, durum and soft wheat) and contrast the model’s performance with machine learning algorithms and conventional benchmark models used in a previous study. Simple benchmarks such as peak NDVI remained challenging to outperform while machine learning models were superior to deep learning models for all forecasting months and all tested crops. We attribute the poor performance of deep learning to the small size of the dataset available.

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Spatiotemporal analysis of rainfed cereal yields across the eastern high plateaus of Algeria: an exploratory investigation of the effects of weather factors

Cited by 6 publications

References 40 publications

Long-term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain)

Long-term changes in rainfed olive production, rainfall and farmer’s income in Bailén (Jaén, Spain)

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Is deeper always better? Evaluating deep learning models for yield forecasting with small data

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