INVESTIGADORES
ALVAREZ Roberto
artículos
Título:
Developments of a soil productivity index using an artificial neural network approach
Autor/es:
J DE PAEPE; . R. ALVAREZ
Revista:
AGRONOMY JOURNAL
Editorial:
AMER SOC AGRONOMY
Referencias:
Año: 2013 vol. 105 p. 1803 - 1813
ISSN:
0002-1962
Resumen:
Soil productivity
indices represent ratings of potential plant biomass production of soils.
Inductive approaches determine productivity based on inferred effects of soil
properties on yield. Conversely, deductive approaches use yield information to
estimate productivity. Our objective was to compare the performance of both
types of productivity indices for assessing regional soil productivity for
wheat yield in the Pampas. Soil data from soil
surveys and interpolated climate information were employed. Wheat yield data
from a 40-yr period and representing approx. 45 Mha were used. Inductive
productivity indices showed a low correlation with observed yield (R2
< 0.45, P = 0.05). The best
performance of deductive empirical methods used was attained using a blind
guess option but soils could only be rated when yield data was available. Yield
models based on the neural network approach had a good performance (R2
= 0.614, root mean square error (RMSE) = 548 kg ha-1) and was used for regional productivity index
development. This index could be extrapolated to soils for which yield data
were not available and its validation with yield averages was optimal (R2
= 0.728; P = 0.05). Regional high
productivity was achieved for combinations of medium to high levels of soil
organic carbon and soil available water storage capacity (SAWSC) variables
which showed a positive interaction. This methodology for assessing soil
productivity based on an empirical yield-based model may be applied in other
regions of the World and for different crops.