Climate services for agriculture typically predict grain yields, but grasslands are overlooked. In the Massif Central, where meadows cover up to 60% of agricultural land and support dairy farming, uncertainty about forage hits the economy. This work stands apart: neighboring papers in the archive deal with weather forecasting, Martian climate swings, or species distribution, but no one has linked oceanic teleconnections to grass yields in Europe. The authors fill this gap, relying on a long modeling run rather than short observations.
They used the biogeochemical pasture model PaSim, fed with hourly meteorological data from the SAFRAN reanalysis over 57 years. They simulated different cutting regimes, from one to three cuts, with and without irrigation, as well as 'optimal management' mimicking farmer practices. Yield was considered potential, meaning without pests and diseases, to isolate a pure climatic signal. They then looked for correlations between yield anomalies and large-scale circulation indices: NAO, El Niño, Mediterranean and tropical Atlantic temperatures. For forecasting, they built a stepwise multiple regression with cross-validation, leaving one year out for testing.
Simulated yield increased by 29% between the periods 1959–1979 and 1995–2015, consistent with warming, rising CO2, and increased solar radiation. On an interannual scale, productivity depended most strongly on radiation and temperature, while precipitation had a weak negative effect due to the already humid climate. Stable correlations were found with Mediterranean temperatures in February and September, and for the three-cut scenario also with the tropical Atlantic from September of the previous year to January, and with El Niño in May–June. The predictive model for three cuts gave a correlation of 0.55 (p<0.01), a root mean square error of 14.6%, and 71.43% hits in the 'above/below average' category, against a baseline probability of 50%. For optimal management, the metrics were more modest: correlation 0.41, error 17.6%, hits 67.86%.
This is the first model-based evidence that large-scale oceanic anomalies can drive grass yields in Europe. Unlike neighboring studies, which either describe weather or Martian climate, this one builds a bridge from ocean temperature to a specific agroecosystem indicator. The result provides a basis for seasonal forage forecasting, which could change planning in livestock farming. However, the relationships are non-stationary: the predictive power of El Niño varied across decades, so caution is needed.
The topic could evolve toward operational forecasting systems for all of Europe based on distributed pasture modeling. Validation against real data, inclusion of grazing rather than only cutting, and accounting for non-stationarity of teleconnections will be required. It could also be combined with remote sensing and farm economic models for a full decision-support cycle.
The results will affect climate services for agriculture, risk management in livestock farming, insurance, and government planning for food security.
Validation of the model against observed yields in Laqueuille and other European grasslands, as well as extension to grazing scenarios and regional scales.
For agricultural meteorology, the non-stationarity of links between climate indices and yield remains an open question: predictive power can disappear for decades. Also unresolved is the transition from potential to actual productivity, where pests, diseases, and management decisions intervene.
This section is addressed to the author of the paper and their colleagues. Neighbouring works were found by meaning-based search across our archive, not by word overlap. We do not review: we show the field around the work.
What the work does
The study models the productivity of a permanent grassland in Laqueuille (France) over 57 years using the biogeochemical model PaSim and SAFRAN meteorological data, isolating the purely climatic signal of yield. The authors then link yield anomalies to large-scale circulation indices (NAO, El Niño, Mediterranean Sea temperature, and tropical Atlantic temperature) and build a statistical forecasting model with cross-validation, achieving 71% accuracy in predicting the 'above/below average' category for a three-cut scenario.
Where its strength is
For the first time in Europe, a link between oceanic teleconnections and grassland yields is demonstrated, based on a long model run rather than short observations, providing a basis for seasonal forecasting of forage resources.
Where it could go next
Worth trying
Why this may matter
If seasonal forecasts of grassland productivity become reliable, they could help farmers in the French Massif Central and other regions plan forage resources in advance, reducing economic risks in livestock farming.
Nearby works in our archive
The archive is sparse around this work: the closest review is only 0.54, against a usual 0.64. It looks like few have drilled nearby.
The meadow in Laqueuille sits at an altitude of 1040 meters, and its climate is classified as subarctic: sunlight is so scarce that radiation, not precipitation, limits grass growth. So in sunny years, yields are higher, even though intuitively rain seems more important.