Forage Production of the Argentine Pampa Region Based on Land Use and Long-Term Normalized Difference Vegetation Index Data

Abstract Information about forage productivity and its interactions with cultural practices or climatic variation is necessary to plan livestock management and to increase production without damaging the environment. Remote sensing provides a valuable data source to achieve these goals. Here we characterize forage production over a large region (92 million hectares) by analyzing spatial, seasonal, and interannual variability with Normalized Difference Vegetation Index (NDVI) data. We identified 23 homogeneous zones that enclose multiple counties with similar characteristics of land use and productivity. A long-term series (1981–2000) of Advanced Very High Resolution Radiometer images were used to calculate monthly NDVI and the annual integral of NDVI (I-NDVI), which is an estimate of primary productivity, for each county. County agricultural land use data were used to resolve pure forage and crop NDVI patterns over time using a spectral unmixing model. The annual integral of NDVI was significantly associated with geographic longitude and average precipitation but not with latitude. Improved relationships between forage production and I-NDVI can be obtained by collecting more accurate forage estimates in the field and calculating radiation use efficiencies. Images of high temporal resolution allow the inference of seasonal changes, and images of high spatial resolution allow a more precise description of the forage resources.

[1]  W. K. Lauenroth,et al.  Review and assessment of various techniques for estimating net aerial primary production in grasslands from harvest data , 1975, The Botanical Review.

[2]  C. D. Di Bella,et al.  Landscape, soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands, Argentina , 2005 .

[3]  C. Rebella,et al.  Remote sensing capabilities to estimate pasture production in France , 2004 .

[4]  Ana M. Cingolani,et al.  Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units , 2004 .

[5]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[6]  Marc L. Imhoff,et al.  Global patterns in human consumption of net primary production , 2004, Nature.

[7]  Osvaldo E. Sala,et al.  PATTERNS AND CONTROLS OF PRIMARY PRODUCTION IN THE PATAGONIAN STEPPE: A REMOTE SENSING APPROACH† , 2002 .

[8]  O. Sala,et al.  Current Distribution of Ecosystem Functional Types in Temperate South America , 2001, Ecosystems.

[9]  O. Sala.  12 – Productivity of Temperate Grasslands , 2001 .

[10]  J. C. Winslow,et al.  Numerical Terradynamic Simulation Group 3-2001 Mapping Weekly Rangeland Vegetation Productivity Using MODIS Algorithms , 2018 .

[11]  G. Posse,et al.  Environmental controls of NDVI and sheep production in the Tierra del Fuego steppe of Argentina , 2000 .

[12]  J. Paruelo,et al.  Estimation of primary production of subhumid rangelands from remote sensing data , 2000 .

[13]  J. Paruelo,et al.  Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina , 2000 .

[14]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[15]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[16]  José M. Paruelo,et al.  Interannual variability of NDVI and its relationship to climate for North American shrublands and grasslands , 1998 .

[17]  M. Oesterheld,et al.  RELATION BETWEEN NOAA‐AVHRR SATELLITE DATA AND STOCKING RATE OF RANGELANDS , 1998 .

[18]  M. S. Rasmussen.  Developing simple, operational, consistent NDVI-vegetation models by applying environmental and climatic information : Part I. Assessment of net primary production , 1998 .

[19]  A. Bondeau,et al.  Combining agricultural crop models and satellite observations: from field to regional scales , 1998 .

[20]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[21]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[22]  J. Paruelo,et al.  ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .

[23]  A. Fischer,et al.  Predicting Crop Reflectances Using Satellite Data Observing Mixed Pixels , 1997 .

[24]  C. Rao,et al.  Inter-satellite calibration linkages for the visible and near-infared channels of the Advanced Very High Resolution Radiometer on the NOAA-7, -9, and -11 spacecraft , 1995 .

[25]  Pierre Hiernaux,et al.  A regional Sahelian grassland model to be coupled with multispectral satellite data. II: Toward the control of its simulations by remotely sensed indices , 1995 .

[26]  H. Kerdiles,et al.  NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa , 1995 .

[27]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[28]  S. Kalluri,et al.  The Pathfinder AVHRR land data set: An improved coarse resolution data set for terrestrial monitoring , 1994 .

[29]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[30]  A. Fischer.  A model for the seasonal variations of vegetation indices in coarse resolution data and its inversion to extract crop parameters , 1994 .

[31]  O. Sala,et al.  Effect of animal husbandry on herbivore-carrying capacity at a regional scale , 1992, Nature.

[32]  A. Soriano.  Rio de la Plata grasslands , 1992 .

[33]  R. Coupland,et al.  Natural grasslands : introduction and Western Hemisphere , 1992 .

[34]  Timothy G. F. Kittel,et al.  Regional Analysis of the Central Great Plains , 1991 .

[35]  Daniel Deybe,et al.  A regional agricultural model using a plant growth simulation program as activities generator— an application to a region in Argentina , 1991 .

[36]  S. McNaughton,et al.  Primary and Secondary Production in Terrestrial Ecosystems , 1991 .

[37]  Jonathan J. Cole,et al.  Comparative Analyses of Ecosystems: Patterns, Mechanisms, and Theories , 1991 .

[38]  A. S. Belward,et al.  A comparison of supervised maximum likelihood and decision tree classification for crop cover estimation from multitemporal LANDSAT MSS data , 1987 .

[39]  J. L. Hatfield,et al.  Sensitivity of spectral vegetative indices to crop biomass , 1987 .

[40]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

[41]  J. W. Kidson,et al.  Determination of seasonal and interannual variation in New Zealand pasture growth from NOAA-7 data , 1985 .

[42]  C. Justice,et al.  Analysis of the phenology of global vegetation using meteorological satellite data , 1985 .

[43]  Compton J. Tucker,et al.  Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel - 1980-1984 , 1985 .

[44]  Compton J. Tucker,et al.  A critical review of remote sensing and other methods for non-destructive estimation of standing crop biomass , 1980 .

[45]  W. Lauenroth.  Grassland Primary Production: North American Grasslands in Perspective , 1979 .

[46]  D. Chestnutt,et al.  The effect of cutting frequency and applied nitrogen on production and digestibility of perennial ryegrass , 1977 .

[47]  J. Monteith.  Climate and the efficiency of crop production in Britain , 1977 .