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Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil
Journal Pre-proof Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil Gabriel Alves Veloso, Manuel Eduardo Ferreira, Laerte Guimarães Ferreira Júnior, Bernardo Barbosa da Silva PII:
S2352-9385(19)30291-5
DOI:
https://doi.org/10.1016/j.rsase.2020.100288
Reference:
RSASE 100288
To appear in:
Remote Sensing Applications: Society and Environment
Received Date: 20 August 2019 Revised Date:
7 December 2019
Accepted Date: 11 January 2020
Please cite this article as: Veloso, G.A., Ferreira, M.E., Ferreira Júnior, Laerte.Guimarã., Barbosa da Silva, B., Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil, Remote Sensing Applications: Society and Environment (2020), doi: https:// doi.org/10.1016/j.rsase.2020.100288. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier B.V. All rights reserved.
FEDERAL UNIVERSITY OF GOIÁS Image Processing and GIS Lab December 6, 2019
Author Contribution Statement
“Modelling Primary Gross Productivity in Tropical Savanna Pasturelands for Livestock Intensification in Brazil” Gabriel Alves Veloso, Manuel Eduardo Ferreira, Laerte Guimarães Ferreira Junior were responsible for the Conceptualization, Methodology, Data curation, Writing- original draft preparation, and Investigation. Bernardo Barbosa da Silva was responsible for the Analysis model supervision and Writing- original draft preparation.
Dear editor, Dr. George Xian, Thank you very much for your kind attention and consideration. I look forward to seeing our manuscript accepted for publication in the prestigious Remote Sensing Applications: Society and Environment. Sincerely,
Gabriel Alves Veloso Remote Sensing and GIS Lab (LAPIG) Universidade Federal de Goiás (UFG) Campus Samambaia Goiânia, GO, 74690-900, Brazil [email protected] Visit: www.lapig.iesa.ufg.br
Modelling Gross Primary Productivity in Tropical Savanna Pasturelands for Livestock Intensification in Brazil aGabriel
Alves Veloso, bManuel Eduardo Ferreira, bLaerte Guimarães Ferreira Júnior, cBernardo Barbosa da Silva
aFederal
University of Pará (UFBA), Geography Departament, Altamira, PA, Brazil Processing and GIS Laboratory (LAPIG), Federal University of Goiás (UFG), Goiânia, GO, Brazil cAtmospheric Sciences Department, Federal University of Campina Grande, Campina Grande, PB, Brazil bImage
ABSTRACT In the grass-fed cattle ranching sector, precise and accurate information on the available dry biomass for consumption is of fundamental importance for the economically and environmentally sustainable meat production. In extensive pasture areas, as those found in Brazil, the use of orbital remote sensing data can be instrumental for the fast retrieval of biophysical parameters related to biomass production, based on which the livestock intensification potential can be determined. Within this context, in this study we estimated the dry biomass production of the State of Goiás (located in the Cerrado biome) pasturelands based on the use of a new highly precise and accurate pasture map and MODIS MOD13Q1 NDVI images (normalized difference vegetation index). Gross primary productivity (GPP) estimates, at 250m spatial resolution, were derived based on the light use efficiency of Brachiaria brizantha (dominant pasture species in the Cerrado), which determines the amount of Absorbed Photosynthetically Active Radiation (APAR) that is utilized in carbon fixation. Considering the current Animal Unit (AU) per hectare and the estimated forage production, the cattle herd in Goiás could double (reaching about 40 million heads) without the need of expanding the existing pasture areas; i.e., with the appropriate information (as the one derived in this study), proper management practices, and adequate public policies, cattle ranching in Goiás (and in Brazil) can be substantially improved (regarding productivity gains), while preventing new deforestations and increases in GHG emissions. Keywords: Pasture; Dry Biomass; Livestock Intensification; Cerrado Biome; MODIS
1. Introduction In Brazil, livestock plays a significant role for the economy, accounting for approximately 31% of the Gross Domestic Product (GDP) of agribusiness. In fact, beef exports represented 3.2% of everything the country exported in 2017, moving 523.25 billion that year (ABIEC, 2018). It should also be noted that Brazil is the second largest exporter of beef in the world, with the largest commercial herd, estimated at about 222 million heads (IBGE, 2018). This production of meat is mainly carried out in extensively cultivated pastures (in general, Brachiaria species), with an estimated area of 175 million hectares (PARENTE et al., 2017), of which, approximately 70 million hectares have some level of degradation (ARAÚJO et al., 2017; PEREIRA et al., 2018). Therefore, and in spite of its expressive figures, the Brazilian cattle ranching continues to be considered of low productivity (average of 1.1 heads / hectare), in part due to grass degradation, with low organic matter or exposed soil with erosive processes. Such conditions substantially reduces the production of biomass, or its resistance and resilience to droughts, which particularly affects the Brazilian savanna biome, known as Cerrado. The increase in productivity in areas with low efficiency depends on the grass support capacity, which can be defined as the adequate (maximum) stocking rate that allows the weight gain per animal / area, maintained at good pasture conditions (SANTOS et al., 2002). This is one of the major challenges regarding grass-based cattle ranching, since productivity varies spatially and temporally, according to soil type, climatic seasonality, management type and forage species (EUCLIDES FILHO et al., 1997). The absence of efficient management of pasture areas, along with high animal density, can lead to the degradation of these areas, compromising animal production and its future support capacity (SANTOS et al., 2002). Therefore, for better performance in the sector, it is necessary to estimate the productivity of grazing areas throughout the country, in order to achieve adequate and sustainable management. And based on productivity estimates, it is possible
to evaluate the intensification potential, which is key for increasing the production of meat without expanding land conversion. In addition, the precise knowledge of the intensification potential is instrumental for the recovery of pasture areas, through improved management and replacement of soil nutrients (e.g. via integrated pasture-cropforest systems - Dias-Filho, 2011), which, in turn, prevent soil losses and / or reduce greenhouse gas emissions (BUSTAMANTE et al., 2012) . Different methodologies can be used for estimating the intensification potential (support capacity) of a given pasture area. Strassburg et al., (2014) estimated the Brazilian stocking rate to be about 0.85 Animal Units (AU) ha-1 and the potential load capacity in the range of 2.37 to 2.53 AU ha-1, according to three scenarios of production of dry matter. Long et al., (2010) estimated the pasture support capacity in China, specifically in the Golong region, based on the aboveground biomass, per capita daily consumption, and number of heads per area. However, these methodologies, based on large spatial generalizations, may present great inconsistencies, since they do not take into account the regional variabilities and specific conditions of the pasture areas. In this sense, and considering the extensive nature of the Brazilian pastures, remote sensing techniques become essential, contributing to the estimation of biophysical parameters in large areas, as well as assisting in the identification of areas with low productivity. Arantes et al., (2018) estimated the potential support capacity of the Brazilian pasturelands based on the MOD17A2H product (primary productivity images derived from the MODIS orbital sensor data) (RUNNING & ZHAO, 2015); however, these estimates were largely affected by the coarse spatial resolution of the MOD17A2H product (i.e., 500 meters). As land use in Brazil is very dynamic (PARENTE et al., 2019), images with low spatial resolution tend to "group" a significant number of landcover and land-use classes, causing errors in the estimates. Another important point is that the climatic calibration of the model utilised in the MODIS product is performed with meteorological data from reanalysis, which can
generate significant errors for analyzes where finer detail is needed. Finally, calibration of the biophysical data (i.e., light efficiency) considers global estimates, which may result in errors due to the wide variety of environments with peculiarities regarding climate, soil, type of vegetation, etc. Therefore, the use of local parameters, considering the dominant pasture species in the region of interest, becomes mandatory for more precise and accurate productivity estimations. Likewise, the use of higher spatial resolution images, such as those provided by the 250 m MODIS vegetation index product (MOD13Q1 - HUETE et al., 2002), can provide a better understanding of the pasture intensification potential on a regional scale, and help in the management and planning of these areas, in order to make them more productive and environmentally sustainable, especially in terms of ecosystem services. In this context, this work focuses on the elaboration of a strategy to quantify the potential for intensification of pastures on a regional scale, especially in pasture areas in the Cerrado biome. Specifically, the study was
carried out in the State of Goiás, due to its centrality and importance in beef production in Brazil, with a herd of 23 million heads (10% of the national herd), being the third largest producer in the country (ABIEC, 2018). Our study, while centered on the MOD17A2H product approach, brings major contributions to the estimation of pasture productivity, by considering higher spatial resolution, local climatic calibration, and specific biophysical parameters for Brachiaria. 2 . Data and Methods 2.1 Study area Located in central Brazil, the state of Goiás is bordered to the north by the state of Tocantins, to the east and southeast by the state of Minas Gerais (MG), to the east by the state of Bahia (BA), to the southwest by the state of Mato Grosso do Sul (MS), and to the west with the state of Mato Grosso (MT) (figure 1). It has 246 municipalities and a total area of 340,086.69 km², being the 7th Brazilian state in territorial extension (IBGE, 2019).
Figure 1. Relative location of the State of Goias in Brazil and in the Cerrado biome (a); Distribution of pasture areas and weather stations (black circles) in the State of Goiás.
Goiás is the only state totally inserted in the core Cerrado region, as well as the fifth most anthropized, with approximately 55% of its original territory converted to agriculture (15%) and cultivated pastures (40%) (GARCIA et al., 2013; SCARAMUZZA et al., 2017). Its relief is formed predominantly by flat lands (Chapadões), which correspond to approximately 65% of the territory, presenting four regional surfaces: a) between 1100 and 1600 m of altitude; b) between 900 and 1000 m; c) between 650 and 1000 m; and d) between 250 and 550 m (OLIVEIRA, 2014). As for the soils, Oxisols are are the most extensive. Concerning the climate, the state has two well-defined seasons: one with high levels of rainfall (1100 to 2100 mm), between October and April, in which approximately 95% of the annual precipitation occurs; and another with low levels of rainfall (20 to 200 mm), between May and September. In relation to the temperature, it has the highest values in the months of August and September (with averages around 34 °C), especially in the northwest of the state, while the lowest temperatures occur in the months of June and July (averages around 12 °C) (CARDOSO et al., 2014). The vegetation of the state presents a predominance of savanna physiognomies, with grassland, shrubland and woody formations covering approximately 90% of the territory, with some small areas of forest formation, known as Mato Grosso goiano (CARDOSO et al., 2014). These climatic, soil, and relief characteristics favored the development of cattle ranching, one of the main economic activities in the state. In spite of the moderately low stocking rates, Goiás ranks third in cattle production in the country (OLIVEIRA, 2014), with a herd of approximately 25 million cattle heads. 2.2 Database For this study, and considering the whole state of Goiás, the following databases were utilized: • MODIS MOD13Q1 and MOD15A2H images, made available by the US Geological Survey
(USGS), for the entire year of 2015 (beginning January 1, and for every 16-days time interval); • Time series of ground meteorological data from the National Institute of Meteorology (INMET) (whose dates corresponded to those of the MODIS composites); • Pasture area map, provided by the Image Processing and GIS Lab (Lapig / UFG, available at: http://maps.lapig.iesa.ufg.br/lapig.html). 2.3 Analysis approaches The estimation of the intensification potential of the pasture areas in the state of Goiás relied on the relationship between the pasture support capacity and the cattlestocking rate, derived from the cattle herd composition (according to the 2006 Agricultural Census) and the total cattle in 2015 (as informed by the Produção Pecuária Municipal - PPM (ARANTES et al., 2018; IBGE, 2015). In order to estimate the support capacity, it was necessary to obtain information about the dry matter production in pasture areas, as well as the corresponding bovine demand for consumption. Dry matter production was estimated from the gross primary productivity (GPP), based on the Absorbed Photosynthetically Active Radiation (APAR) and the light use efficiency (LUE) by plants, which is the rationale behind the MOD17A2H product (eq. 1): = ∗
(eq. 1)
Intended for global applications, the MODIS Product uses a single LUE value; i.e., a pasture in Brazil has the same LUE value of a pasture in China, of 0.86 g C MJ-1 (MOD17A2H User Guide, 2015). On the other hand, and as shown by Fonseca et al. (2006), Rosa and Sano (2013), and Machado (2014), LUE values in Brachiaria pastures in the Cerrado biome are in the order of 0.50 g C MJ-1, , which was the value utilized in our study. The LUE maximum value (i.e., 0.50 in our study) is attenuated by several environmental factors, such as air temperature
(Tmin_scalar) and vapor pressure deficit (DPV_scalar) (eq. 2): = ∗ ∗ (eq. 2) In our study, these environmental attenuation parameters were estimated with local meteorological data, where Tmin scalar is determined as a function of two extreme temperature values of the air, for which LUE = 0.5 and LUE = 0, denominated (Tmax) and (Tmin), respectively; Tmax represents the air temperature value which allows LUE to reach its maximum value (Tmax = 12.02 °C); and Tmin represents the air temperature value which causes LUE to be zero (Tmin = -8.00 °C); T_scalar assumes values between 0 and 1 for air temperature values between Tmin and Tmax, respectively, being a linear function directly proportional to the air temperature throughout the day. As for the DPV_scalar, it is an attenuation factor as a function of two extreme values of vapor pressure deficit (DPV), for which LUE = 0.5 and LUE = 0, denominated DPVmin and DPVmax, respectively; DPVmin represents the value of DPV that allows LUE to reach its maximum value (DPV = 650 Pa), while DPVmax represents the value of DPV that causes LUE to be equal to zero (DPV = 5300 Pa); DPV_scalar assumes values between 0 and 1, according to DPVmax and DPVmin values, respectively, being a linear function inversely proportional to the DPV throughout the day. These values reflect the influence of air temperature and DPV on the light-use behavior, as limiting factors of this process (MOD17A2H User Guide, 2015; FRIEDL et al., 2010). Another parameter estimated with local meteorological data in our study was the Photosynthetically Active Radiation (PAR). The PAR corresponds to the fraction of the global solar radiation spectrum, between 400 and 700 nm, used in the process of photosynthesis (FINCH et al., 2004). In the absence of PAR measurements, this can be estimated as a function of the incident short-wave solar radiation, in which only a fraction of PAR is absorbed in the atmospheric carbon fixation process. For the estimation of PAR in this work, the value of 0.58 was adopted as a factor for
the use of sunlight. In the literature, it is estimated that the PAR fraction used by the vegetation in its process of photosynthesis is approximately 50%. Studies by Galvani (2009) indicate ratios between 44% and 69%, while Silva et al. (2013) indicate that, for the Cerrado region, the value of photosynthetically active radiation represents 58% of the global radiation, applicable to dry and rainy seasons (eq. 3): = 0.58 ∗ (eq. 3) The meteorological stations considered in this study are shown in figure 1. The climatic data were interpolated by the inverse distance power (IDP) technique, which is a weighted average interpolator, so that the influence of one point relative to the other decreases with distance. Regarding the APAR, this was based on the Fraction of Photosynthetically Active Absorbed Radiation - FPAR, as follows (eq. 4): = ∗ (eq. 4) On the other hand, the FPAR was based on the normalized difference vegetation index (NDVI) images, obtained from the 250m spatial resolution MODIS MOD13Q1 product, as several studies have demonstrated a high correlation between FPAR and vegetation indices, such as NDVI (SELLERS et al., 1992; POTTER et al., 1993; SELLERS et al., 1997 and DEFRIES et al., 1997). Therefore, for FPAR estimation in this work, we first considered the correlation between the 250 m MOD13Q1H (NDVI) and the 500 m MOD15A2H (FPAR) products over pasture areas, for the year 2015. The coefficients obtained through the regressions for the dry- and wet-season months were then applied to the MOD13Q1 NDVI images (eqs. 5 and 6): FPAR = 0.0564 + 0.8198 * NDVI (wet season; r = 0.6108 and p value < 0.0001) (eq. 5) FPAR = 0.004 + 0.9843 * NDVI (dry season; r = 0.8178 and p < 0.0001) (eq. 6)
Another important improvement in this work was the use of the new pasture map for Brazil, based on the automated classification of 30m spatial resolution Landsat 8 images (PARENTE et al., 2017; PARENTE et al., 2019). The use of this map denotes a great improvement, since the MODIS GPP data is based on the 500m MOD12Q1 global product (HEINSCH et al., 2006). For the conversion of GPP (in g C m-2 -1 day ) into dry biomass, we considered a carbon - biomass conversion factor of 2.7, as determined by Neumann Cosel et al. (2011) (eq. 7): ! = ∗ 2.7
(eq. 7)
With this, the capacity of bovine support was obtained from the relation between the estimated dry biomass and the pasture demand for an animal unit (1 UA = 450 kg), which corresponds to twice its consumption. Arantes
et al. (2018) argue that the daily intake of dry matter is, on average, 2.5% of the animal live weight (450 kg), i.e., 11.25 kg of dry matter; and that the demand for fodder needs to be twice as large as the consumption, since some of the available fodder is not used (when trampled). Thus, a daily demand of 22.5 kg of dry matter for an animal, with an average weight of 450 kg (ANDRADE et al., 2017), was obtained. Finally, to estimate the potential for intensification of pasture areas in the state of Goiás, the current bovine stocking rate was subtracted from the carrying capacity. The datasets and methodological steps considered in this study are depicted in figure 2. All the images were processed using ERDAS imagine software, in which the mathematical operations were performed using the Model Maker tool.
Figure 2. Flowchart depicting the main datasets and methodological approaches utilized in this study.
3. Results and Discussion
The monthly dry biomass variation in the state of Goias pasture areas is shown in figure 3. Increasing biomass values can be observed
from January to April (when it reaches the highest values), while decreasing values in biomass begin in May, reaching the lowest values in the month of August (usually marked by an intense drought), and returning to increase in the months of September / October (beginning of the wet season). As suggested by the maps shown in figure 3, pasture biomass values in Goiás are strongly influenced by the climatic seasonality, presenting two well-defined moments: a drought that begins in April and runs through September, and a rainy season, from October to March. For the beginning of the dry season (April, May and June), pasture productivity values are mostly high, especially in April , due to the water availability in the soil, optimal temperatures, and high incidence of PAR, being these factors critical for the biomass production.
However, with the prolonged drought conditions, biomass production is reduced, reaching the lowest values in August, mainly due to soil water stress. With the resumption of rainfall in late September and October, the productivity of pasture areas augments, although below that recorded at the beginning of the dry season, as the soil water availability is still low. The influence of the climatic seasonality (and related water availability) on pasture productivity is clearly depicted by both the maps shown in figure 3, as well as the graph shown in figure 4, which shows the 2015 monthly precipitation. This result corroborates with the Churkina and Running (1998) findings, who demonstrated that water availability is a determinant factor in the productivity of C4 pastures worldwide.
Figure 3. Monthly distribution of dry biomass in the State of Goiás pasturelands, for the year 2015, based on gross primary productivity values estimated in this study.
The lowest pasture productivity values (<= 1000 kg ha-1) were found in the northwest region of Goiás, mainly in August (figure 3). This result may also be associated with the huge grazing pressure of this region, with a
herd estimated at 4.8 million heads of cattle for the year 2015 (IBGE, 2017), corresponding to approximately 23% of the cattle herd in the state.
Figure 4. Average monthly dry biomass yield for the State of Goiás pastures, relatively to the average monthly rainfall distribution.
On the other hand, this region presents good productivity (values above 1500 kg dry matter / ha) in the wet season months, what suggests good pasture quality, a possible consequence of the investments made by the Federal Government within the scope of the Low Carbon Agriculture program (in operation from 2010 until mid-2019, known in Brazil as Programa ABC, or Agricultura de Baixo Carbono). In fact, the mesoregions of northwestern Goiás, followed by the southern region, were the ones that received the most investments for the recovery of degraded pasture areas in the state of Goiás (ROSA, 2018 ). This argument validates the claims of Yang et al. (2016), that the restoration of exhausted pasture areas in China, due to the effectiveness of environmental protection programs, resulted in increased productivity gains, while reducing GHG emissions from this sector. Brazil seeks to follow the same path, stimulated by recent climate agreements signed with the United Nations / IPCC member countries (BARROS, 2017). Overall, the average pasture productivity in Goiás was approximately 1600 Kg of dry matter / ha / month (with an average standard deviation of 272.8 kg/Ha/month). Brito et al. (2018), using satellite-based ground data extrapolations, found average monthly values of approximately 2100 kg / ha of green biomass for the Cerrado pasture areas, in addition to a high correlation between rainfall regimes and pasture productivity,
corroborating the influence of climatic seasonality on biophysical productivity responses. Andrade et al. (2009), in a work carried out with NDVI images obtained from the Landsat 5 - TM satellite, for Brachiaria brizantha pasture areas at the Experimental Livestock Farm in Campo Grande - MS, obtained average monthly dry biomass values of 1981 Kg / ha. Rodrigues et al. (2011), in an experiment in the municipality of Nova Odessa, São Paulo, found average values of monthly pasture productivity of approximately 1450 Kg of dry matter / ha. Rosa and Sano (2013), in a study carried out in the Paranaíba River basin, in Minas Gerais state, indicated an average productivity of approximately 1000 kg of dry matter / ha / month. These differences may be related to several factors, such as soil fertility, water infiltration and storage capacity, and site-specific temperature and precipitation patterns. Figure 5 shows the potential stocking rate distribution in Goiás, according to the GPP model developed in this study, i.e., based on the use of 250 m MOD13Q1 NDVI images, local calibration parameters, and a high spatial resolution pasture map. It should be emphasized that the derivation of the potential bovine stocking rate is of fundamental importance for the implementation of public policies , aiming at increasing the support capacity of pasturelands .
Figure 5. Distribution of Animal Unit (AU) / Hectare for the State of Goiás, during 2015, based on the approach developed in this study.
Specifically, our bovine support capacity map showed a variation between <= 1.5 and 3.5 AU / ha, with average values close to 2.5 AU / ha, which is well above the current stocking rates found in most regions of Goiás, of about 1.2 AU / ha (EUCLIDES FILHO et al., 1997). The highest values of potential stocking rates were observed mainly in the central, southern and extreme northeast portions of the state, while the lowest values occurred predominantly in the northern and northwestern portions of the state (Araguaia river depression). However, in the northwestern portion, values of AU / ha between 2.5 and 3 were found especially in the municipalities of São Miguel do Araguaia, Novo Mundo, Novo Planalto, Novo Crixás, and Bonópolis (in the extreme northwest), which currently receive economic stimulus from the Programa ABC. The highest UA values were recorded in the northeastern region of the state, characterized by some terrain ondulation and
shallow soils. Thus, overestimated figures, as well as the need of additional investigations, can not be discarded. The accuracy of the land use map is of vital importance in the modeling of dry biomass, because if native vegetation areas are considered as pasture areas, these may have their values overestimated. However, this region of the Northeast of Goiás is a more conserved area due to the sloppy terrain, making it more difficult for the cattle to access, what can facilitate the accumulation of biomass in both native and cultivated pastures. In relation to the intensification potential of cattle raising for the State of Goiás (figure 06), it presented an average potential of 1.19 AU / ha, with the northeast, north and south regions having the highest values (1.5 to 3 AU / ha) of intensification potential. In other words, we could double the herd size in Goiás, without the need of converting additional Cerrado areas (i.e., avoiding new deforestations and soil degradation).
Figure 6. Distribution of the livestock intensification potential for the state of Goiás (referring to the year 2015).
The areas with low intensification potential may be related to the process of pasture degradation, a circumstance very common in the tropical pasture ecosystems (DIAS-FILHO, 2011). According to Dias-Filho (2011), major causes of pasture degradation, usually associated with a critical loss of soil fertility, include: a) indiscriminate and excessive fire use; b) pests and diseases; and c) climatic factors, when, in the event of prolonged droughts, can occur the emergence and / or development of invasive species, or, in case of excess humidity in the rainy season, can occur the proliferation of insect pests and diseases, as well as the loss of soil fertility through the acceleration of erosion and leaching processes. Another determining factor behind pasture degradation is the inadequate management, characterized by high stocking rates, grazing at non-optimal time intervals, no periodic replenishment of fertilizers in the soil, excessive use of fire, seeds of low cultural value and sowing in the improper season (DIASFILHO, 2011). Pasture degradation causes a substantial reduction in biomass production,
while greenhouse gas emissions tend to increase. On the other hand, pasture restoration can lead to both productivity and environmental gains. Crop-Livestock-Forest integrated systems have been one of the alternatives tested in the Cerrado, along with the correct number of cattle per hectare, a decision that this study can definitely contribute. 4. Final Considerations This research demonstrated the capability of using orbital remote sensing to estimate the livestock intensification potential in the State of Goiás, with reference to estimates of pasture productivity and bovine support capacity (i.e., Animal Unit / Hectare). Such estimates, based on the use of moderate spatial resolution satellite images and local biophysical parameters, are instrumental for the low cost and fast-track monitoring of the pasture areas in the Cerrado biome. In fact, the approaches developed in this study, as opposed to methods dependent on field biomass sampling,
can be easily extrapolated to the entire pasture areas in Brazil, Our results indicated an average potential stocking rate of about 2.08 AU / Ha, which is substantially higher than the current 1.2 AU / Ha encountered (on average) in the State of Goiás. Considering only this potential biomass productivity, the Goiás cattle herd could double without expanding existing pasture areas . In short, with the appropriate information (as the one derived in this study), proper management practices, and adequate public policies, cattle ranching in Goiás (and in Brazil) can be substantially improved (regarding productivity gains), while preventing new deforestations and increases in GHG emissions.
acuidade espacial dos mapeamentos da área de pastagem para o Brasil. In: XVIII Simpósio Brasileiro de Sensoriamento Remoto, 2017.
5. Acknowledgements
BUSTAMANTE, M.; NOBRE, C.; SMERALDI, R.; AGUIAR, A. P. D.; BARIONI, L. G.; FERREIRA., L. G.; LONGO, K.; MAY, P.; PINTO, A. S.; OMETTO, J. P. H. B. Estimating Greenhouse Gas Emissions from Cattle Raising in Brazil. Climatic Change, v. 115, p. 559-577, 2012.
This work was supported by the Gordon and Betty Moore Foundation (GBMF), The Nature Conservancy (TNC), the Brazilian Research Council (CNPq), and the Coordination for the Improvement of Higher Education Personnel (CAPES / Ministry of Education). . 6. References
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Declaration of Interest I declare that there are no conflicts of interest between the authors of the article entitled “Modeling Primary Gross Productivity in Tropical savanna for Livestock Intensification in Brazil” submitted to the Journal Remote Sensig Applications: Society and Environment.
Altamira, august 19, 2019 Gabriel Alves Veloso Author responsible for submission
Ethics in publishing I declare that the article entitled “Modeling Primary Gross Productivity in Tropical savanna for Livestock Intensification in Brazil” submitted to the Journal Remote Sensig Applications: Society and Environment, followed all ethical practices in its evelopment and writing.
Altamira, august 19, 2019 Gabriel Alves Veloso Author responsible for submission
S2352-9385(19)30291-5
DOI:
https://doi.org/10.1016/j.rsase.2020.100288
Reference:
RSASE 100288
To appear in:
Remote Sensing Applications: Society and Environment
Received Date: 20 August 2019 Revised Date:
7 December 2019
Accepted Date: 11 January 2020
Please cite this article as: Veloso, G.A., Ferreira, M.E., Ferreira Júnior, Laerte.Guimarã., Barbosa da Silva, B., Modelling gross primary productivity in tropical savanna pasturelands for livestock intensification in Brazil, Remote Sensing Applications: Society and Environment (2020), doi: https:// doi.org/10.1016/j.rsase.2020.100288. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Elsevier B.V. All rights reserved.
FEDERAL UNIVERSITY OF GOIÁS Image Processing and GIS Lab December 6, 2019
Author Contribution Statement
“Modelling Primary Gross Productivity in Tropical Savanna Pasturelands for Livestock Intensification in Brazil” Gabriel Alves Veloso, Manuel Eduardo Ferreira, Laerte Guimarães Ferreira Junior were responsible for the Conceptualization, Methodology, Data curation, Writing- original draft preparation, and Investigation. Bernardo Barbosa da Silva was responsible for the Analysis model supervision and Writing- original draft preparation.
Dear editor, Dr. George Xian, Thank you very much for your kind attention and consideration. I look forward to seeing our manuscript accepted for publication in the prestigious Remote Sensing Applications: Society and Environment. Sincerely,
Gabriel Alves Veloso Remote Sensing and GIS Lab (LAPIG) Universidade Federal de Goiás (UFG) Campus Samambaia Goiânia, GO, 74690-900, Brazil [email protected] Visit: www.lapig.iesa.ufg.br
Modelling Gross Primary Productivity in Tropical Savanna Pasturelands for Livestock Intensification in Brazil aGabriel
Alves Veloso, bManuel Eduardo Ferreira, bLaerte Guimarães Ferreira Júnior, cBernardo Barbosa da Silva
aFederal
University of Pará (UFBA), Geography Departament, Altamira, PA, Brazil Processing and GIS Laboratory (LAPIG), Federal University of Goiás (UFG), Goiânia, GO, Brazil cAtmospheric Sciences Department, Federal University of Campina Grande, Campina Grande, PB, Brazil bImage
ABSTRACT In the grass-fed cattle ranching sector, precise and accurate information on the available dry biomass for consumption is of fundamental importance for the economically and environmentally sustainable meat production. In extensive pasture areas, as those found in Brazil, the use of orbital remote sensing data can be instrumental for the fast retrieval of biophysical parameters related to biomass production, based on which the livestock intensification potential can be determined. Within this context, in this study we estimated the dry biomass production of the State of Goiás (located in the Cerrado biome) pasturelands based on the use of a new highly precise and accurate pasture map and MODIS MOD13Q1 NDVI images (normalized difference vegetation index). Gross primary productivity (GPP) estimates, at 250m spatial resolution, were derived based on the light use efficiency of Brachiaria brizantha (dominant pasture species in the Cerrado), which determines the amount of Absorbed Photosynthetically Active Radiation (APAR) that is utilized in carbon fixation. Considering the current Animal Unit (AU) per hectare and the estimated forage production, the cattle herd in Goiás could double (reaching about 40 million heads) without the need of expanding the existing pasture areas; i.e., with the appropriate information (as the one derived in this study), proper management practices, and adequate public policies, cattle ranching in Goiás (and in Brazil) can be substantially improved (regarding productivity gains), while preventing new deforestations and increases in GHG emissions. Keywords: Pasture; Dry Biomass; Livestock Intensification; Cerrado Biome; MODIS
1. Introduction In Brazil, livestock plays a significant role for the economy, accounting for approximately 31% of the Gross Domestic Product (GDP) of agribusiness. In fact, beef exports represented 3.2% of everything the country exported in 2017, moving 523.25 billion that year (ABIEC, 2018). It should also be noted that Brazil is the second largest exporter of beef in the world, with the largest commercial herd, estimated at about 222 million heads (IBGE, 2018). This production of meat is mainly carried out in extensively cultivated pastures (in general, Brachiaria species), with an estimated area of 175 million hectares (PARENTE et al., 2017), of which, approximately 70 million hectares have some level of degradation (ARAÚJO et al., 2017; PEREIRA et al., 2018). Therefore, and in spite of its expressive figures, the Brazilian cattle ranching continues to be considered of low productivity (average of 1.1 heads / hectare), in part due to grass degradation, with low organic matter or exposed soil with erosive processes. Such conditions substantially reduces the production of biomass, or its resistance and resilience to droughts, which particularly affects the Brazilian savanna biome, known as Cerrado. The increase in productivity in areas with low efficiency depends on the grass support capacity, which can be defined as the adequate (maximum) stocking rate that allows the weight gain per animal / area, maintained at good pasture conditions (SANTOS et al., 2002). This is one of the major challenges regarding grass-based cattle ranching, since productivity varies spatially and temporally, according to soil type, climatic seasonality, management type and forage species (EUCLIDES FILHO et al., 1997). The absence of efficient management of pasture areas, along with high animal density, can lead to the degradation of these areas, compromising animal production and its future support capacity (SANTOS et al., 2002). Therefore, for better performance in the sector, it is necessary to estimate the productivity of grazing areas throughout the country, in order to achieve adequate and sustainable management. And based on productivity estimates, it is possible
to evaluate the intensification potential, which is key for increasing the production of meat without expanding land conversion. In addition, the precise knowledge of the intensification potential is instrumental for the recovery of pasture areas, through improved management and replacement of soil nutrients (e.g. via integrated pasture-cropforest systems - Dias-Filho, 2011), which, in turn, prevent soil losses and / or reduce greenhouse gas emissions (BUSTAMANTE et al., 2012) . Different methodologies can be used for estimating the intensification potential (support capacity) of a given pasture area. Strassburg et al., (2014) estimated the Brazilian stocking rate to be about 0.85 Animal Units (AU) ha-1 and the potential load capacity in the range of 2.37 to 2.53 AU ha-1, according to three scenarios of production of dry matter. Long et al., (2010) estimated the pasture support capacity in China, specifically in the Golong region, based on the aboveground biomass, per capita daily consumption, and number of heads per area. However, these methodologies, based on large spatial generalizations, may present great inconsistencies, since they do not take into account the regional variabilities and specific conditions of the pasture areas. In this sense, and considering the extensive nature of the Brazilian pastures, remote sensing techniques become essential, contributing to the estimation of biophysical parameters in large areas, as well as assisting in the identification of areas with low productivity. Arantes et al., (2018) estimated the potential support capacity of the Brazilian pasturelands based on the MOD17A2H product (primary productivity images derived from the MODIS orbital sensor data) (RUNNING & ZHAO, 2015); however, these estimates were largely affected by the coarse spatial resolution of the MOD17A2H product (i.e., 500 meters). As land use in Brazil is very dynamic (PARENTE et al., 2019), images with low spatial resolution tend to "group" a significant number of landcover and land-use classes, causing errors in the estimates. Another important point is that the climatic calibration of the model utilised in the MODIS product is performed with meteorological data from reanalysis, which can
generate significant errors for analyzes where finer detail is needed. Finally, calibration of the biophysical data (i.e., light efficiency) considers global estimates, which may result in errors due to the wide variety of environments with peculiarities regarding climate, soil, type of vegetation, etc. Therefore, the use of local parameters, considering the dominant pasture species in the region of interest, becomes mandatory for more precise and accurate productivity estimations. Likewise, the use of higher spatial resolution images, such as those provided by the 250 m MODIS vegetation index product (MOD13Q1 - HUETE et al., 2002), can provide a better understanding of the pasture intensification potential on a regional scale, and help in the management and planning of these areas, in order to make them more productive and environmentally sustainable, especially in terms of ecosystem services. In this context, this work focuses on the elaboration of a strategy to quantify the potential for intensification of pastures on a regional scale, especially in pasture areas in the Cerrado biome. Specifically, the study was
carried out in the State of Goiás, due to its centrality and importance in beef production in Brazil, with a herd of 23 million heads (10% of the national herd), being the third largest producer in the country (ABIEC, 2018). Our study, while centered on the MOD17A2H product approach, brings major contributions to the estimation of pasture productivity, by considering higher spatial resolution, local climatic calibration, and specific biophysical parameters for Brachiaria. 2 . Data and Methods 2.1 Study area Located in central Brazil, the state of Goiás is bordered to the north by the state of Tocantins, to the east and southeast by the state of Minas Gerais (MG), to the east by the state of Bahia (BA), to the southwest by the state of Mato Grosso do Sul (MS), and to the west with the state of Mato Grosso (MT) (figure 1). It has 246 municipalities and a total area of 340,086.69 km², being the 7th Brazilian state in territorial extension (IBGE, 2019).
Figure 1. Relative location of the State of Goias in Brazil and in the Cerrado biome (a); Distribution of pasture areas and weather stations (black circles) in the State of Goiás.
Goiás is the only state totally inserted in the core Cerrado region, as well as the fifth most anthropized, with approximately 55% of its original territory converted to agriculture (15%) and cultivated pastures (40%) (GARCIA et al., 2013; SCARAMUZZA et al., 2017). Its relief is formed predominantly by flat lands (Chapadões), which correspond to approximately 65% of the territory, presenting four regional surfaces: a) between 1100 and 1600 m of altitude; b) between 900 and 1000 m; c) between 650 and 1000 m; and d) between 250 and 550 m (OLIVEIRA, 2014). As for the soils, Oxisols are are the most extensive. Concerning the climate, the state has two well-defined seasons: one with high levels of rainfall (1100 to 2100 mm), between October and April, in which approximately 95% of the annual precipitation occurs; and another with low levels of rainfall (20 to 200 mm), between May and September. In relation to the temperature, it has the highest values in the months of August and September (with averages around 34 °C), especially in the northwest of the state, while the lowest temperatures occur in the months of June and July (averages around 12 °C) (CARDOSO et al., 2014). The vegetation of the state presents a predominance of savanna physiognomies, with grassland, shrubland and woody formations covering approximately 90% of the territory, with some small areas of forest formation, known as Mato Grosso goiano (CARDOSO et al., 2014). These climatic, soil, and relief characteristics favored the development of cattle ranching, one of the main economic activities in the state. In spite of the moderately low stocking rates, Goiás ranks third in cattle production in the country (OLIVEIRA, 2014), with a herd of approximately 25 million cattle heads. 2.2 Database For this study, and considering the whole state of Goiás, the following databases were utilized: • MODIS MOD13Q1 and MOD15A2H images, made available by the US Geological Survey
(USGS), for the entire year of 2015 (beginning January 1, and for every 16-days time interval); • Time series of ground meteorological data from the National Institute of Meteorology (INMET) (whose dates corresponded to those of the MODIS composites); • Pasture area map, provided by the Image Processing and GIS Lab (Lapig / UFG, available at: http://maps.lapig.iesa.ufg.br/lapig.html). 2.3 Analysis approaches The estimation of the intensification potential of the pasture areas in the state of Goiás relied on the relationship between the pasture support capacity and the cattlestocking rate, derived from the cattle herd composition (according to the 2006 Agricultural Census) and the total cattle in 2015 (as informed by the Produção Pecuária Municipal - PPM (ARANTES et al., 2018; IBGE, 2015). In order to estimate the support capacity, it was necessary to obtain information about the dry matter production in pasture areas, as well as the corresponding bovine demand for consumption. Dry matter production was estimated from the gross primary productivity (GPP), based on the Absorbed Photosynthetically Active Radiation (APAR) and the light use efficiency (LUE) by plants, which is the rationale behind the MOD17A2H product (eq. 1): = ∗
(eq. 1)
Intended for global applications, the MODIS Product uses a single LUE value; i.e., a pasture in Brazil has the same LUE value of a pasture in China, of 0.86 g C MJ-1 (MOD17A2H User Guide, 2015). On the other hand, and as shown by Fonseca et al. (2006), Rosa and Sano (2013), and Machado (2014), LUE values in Brachiaria pastures in the Cerrado biome are in the order of 0.50 g C MJ-1, , which was the value utilized in our study. The LUE maximum value (i.e., 0.50 in our study) is attenuated by several environmental factors, such as air temperature
(Tmin_scalar) and vapor pressure deficit (DPV_scalar) (eq. 2): = ∗ ∗ (eq. 2) In our study, these environmental attenuation parameters were estimated with local meteorological data, where Tmin scalar is determined as a function of two extreme temperature values of the air, for which LUE = 0.5 and LUE = 0, denominated (Tmax) and (Tmin), respectively; Tmax represents the air temperature value which allows LUE to reach its maximum value (Tmax = 12.02 °C); and Tmin represents the air temperature value which causes LUE to be zero (Tmin = -8.00 °C); T_scalar assumes values between 0 and 1 for air temperature values between Tmin and Tmax, respectively, being a linear function directly proportional to the air temperature throughout the day. As for the DPV_scalar, it is an attenuation factor as a function of two extreme values of vapor pressure deficit (DPV), for which LUE = 0.5 and LUE = 0, denominated DPVmin and DPVmax, respectively; DPVmin represents the value of DPV that allows LUE to reach its maximum value (DPV = 650 Pa), while DPVmax represents the value of DPV that causes LUE to be equal to zero (DPV = 5300 Pa); DPV_scalar assumes values between 0 and 1, according to DPVmax and DPVmin values, respectively, being a linear function inversely proportional to the DPV throughout the day. These values reflect the influence of air temperature and DPV on the light-use behavior, as limiting factors of this process (MOD17A2H User Guide, 2015; FRIEDL et al., 2010). Another parameter estimated with local meteorological data in our study was the Photosynthetically Active Radiation (PAR). The PAR corresponds to the fraction of the global solar radiation spectrum, between 400 and 700 nm, used in the process of photosynthesis (FINCH et al., 2004). In the absence of PAR measurements, this can be estimated as a function of the incident short-wave solar radiation, in which only a fraction of PAR is absorbed in the atmospheric carbon fixation process. For the estimation of PAR in this work, the value of 0.58 was adopted as a factor for
the use of sunlight. In the literature, it is estimated that the PAR fraction used by the vegetation in its process of photosynthesis is approximately 50%. Studies by Galvani (2009) indicate ratios between 44% and 69%, while Silva et al. (2013) indicate that, for the Cerrado region, the value of photosynthetically active radiation represents 58% of the global radiation, applicable to dry and rainy seasons (eq. 3): = 0.58 ∗ (eq. 3) The meteorological stations considered in this study are shown in figure 1. The climatic data were interpolated by the inverse distance power (IDP) technique, which is a weighted average interpolator, so that the influence of one point relative to the other decreases with distance. Regarding the APAR, this was based on the Fraction of Photosynthetically Active Absorbed Radiation - FPAR, as follows (eq. 4): = ∗ (eq. 4) On the other hand, the FPAR was based on the normalized difference vegetation index (NDVI) images, obtained from the 250m spatial resolution MODIS MOD13Q1 product, as several studies have demonstrated a high correlation between FPAR and vegetation indices, such as NDVI (SELLERS et al., 1992; POTTER et al., 1993; SELLERS et al., 1997 and DEFRIES et al., 1997). Therefore, for FPAR estimation in this work, we first considered the correlation between the 250 m MOD13Q1H (NDVI) and the 500 m MOD15A2H (FPAR) products over pasture areas, for the year 2015. The coefficients obtained through the regressions for the dry- and wet-season months were then applied to the MOD13Q1 NDVI images (eqs. 5 and 6): FPAR = 0.0564 + 0.8198 * NDVI (wet season; r = 0.6108 and p value < 0.0001) (eq. 5) FPAR = 0.004 + 0.9843 * NDVI (dry season; r = 0.8178 and p < 0.0001) (eq. 6)
Another important improvement in this work was the use of the new pasture map for Brazil, based on the automated classification of 30m spatial resolution Landsat 8 images (PARENTE et al., 2017; PARENTE et al., 2019). The use of this map denotes a great improvement, since the MODIS GPP data is based on the 500m MOD12Q1 global product (HEINSCH et al., 2006). For the conversion of GPP (in g C m-2 -1 day ) into dry biomass, we considered a carbon - biomass conversion factor of 2.7, as determined by Neumann Cosel et al. (2011) (eq. 7): ! = ∗ 2.7
(eq. 7)
With this, the capacity of bovine support was obtained from the relation between the estimated dry biomass and the pasture demand for an animal unit (1 UA = 450 kg), which corresponds to twice its consumption. Arantes
et al. (2018) argue that the daily intake of dry matter is, on average, 2.5% of the animal live weight (450 kg), i.e., 11.25 kg of dry matter; and that the demand for fodder needs to be twice as large as the consumption, since some of the available fodder is not used (when trampled). Thus, a daily demand of 22.5 kg of dry matter for an animal, with an average weight of 450 kg (ANDRADE et al., 2017), was obtained. Finally, to estimate the potential for intensification of pasture areas in the state of Goiás, the current bovine stocking rate was subtracted from the carrying capacity. The datasets and methodological steps considered in this study are depicted in figure 2. All the images were processed using ERDAS imagine software, in which the mathematical operations were performed using the Model Maker tool.
Figure 2. Flowchart depicting the main datasets and methodological approaches utilized in this study.
3. Results and Discussion
The monthly dry biomass variation in the state of Goias pasture areas is shown in figure 3. Increasing biomass values can be observed
from January to April (when it reaches the highest values), while decreasing values in biomass begin in May, reaching the lowest values in the month of August (usually marked by an intense drought), and returning to increase in the months of September / October (beginning of the wet season). As suggested by the maps shown in figure 3, pasture biomass values in Goiás are strongly influenced by the climatic seasonality, presenting two well-defined moments: a drought that begins in April and runs through September, and a rainy season, from October to March. For the beginning of the dry season (April, May and June), pasture productivity values are mostly high, especially in April , due to the water availability in the soil, optimal temperatures, and high incidence of PAR, being these factors critical for the biomass production.
However, with the prolonged drought conditions, biomass production is reduced, reaching the lowest values in August, mainly due to soil water stress. With the resumption of rainfall in late September and October, the productivity of pasture areas augments, although below that recorded at the beginning of the dry season, as the soil water availability is still low. The influence of the climatic seasonality (and related water availability) on pasture productivity is clearly depicted by both the maps shown in figure 3, as well as the graph shown in figure 4, which shows the 2015 monthly precipitation. This result corroborates with the Churkina and Running (1998) findings, who demonstrated that water availability is a determinant factor in the productivity of C4 pastures worldwide.
Figure 3. Monthly distribution of dry biomass in the State of Goiás pasturelands, for the year 2015, based on gross primary productivity values estimated in this study.
The lowest pasture productivity values (<= 1000 kg ha-1) were found in the northwest region of Goiás, mainly in August (figure 3). This result may also be associated with the huge grazing pressure of this region, with a
herd estimated at 4.8 million heads of cattle for the year 2015 (IBGE, 2017), corresponding to approximately 23% of the cattle herd in the state.
Figure 4. Average monthly dry biomass yield for the State of Goiás pastures, relatively to the average monthly rainfall distribution.
On the other hand, this region presents good productivity (values above 1500 kg dry matter / ha) in the wet season months, what suggests good pasture quality, a possible consequence of the investments made by the Federal Government within the scope of the Low Carbon Agriculture program (in operation from 2010 until mid-2019, known in Brazil as Programa ABC, or Agricultura de Baixo Carbono). In fact, the mesoregions of northwestern Goiás, followed by the southern region, were the ones that received the most investments for the recovery of degraded pasture areas in the state of Goiás (ROSA, 2018 ). This argument validates the claims of Yang et al. (2016), that the restoration of exhausted pasture areas in China, due to the effectiveness of environmental protection programs, resulted in increased productivity gains, while reducing GHG emissions from this sector. Brazil seeks to follow the same path, stimulated by recent climate agreements signed with the United Nations / IPCC member countries (BARROS, 2017). Overall, the average pasture productivity in Goiás was approximately 1600 Kg of dry matter / ha / month (with an average standard deviation of 272.8 kg/Ha/month). Brito et al. (2018), using satellite-based ground data extrapolations, found average monthly values of approximately 2100 kg / ha of green biomass for the Cerrado pasture areas, in addition to a high correlation between rainfall regimes and pasture productivity,
corroborating the influence of climatic seasonality on biophysical productivity responses. Andrade et al. (2009), in a work carried out with NDVI images obtained from the Landsat 5 - TM satellite, for Brachiaria brizantha pasture areas at the Experimental Livestock Farm in Campo Grande - MS, obtained average monthly dry biomass values of 1981 Kg / ha. Rodrigues et al. (2011), in an experiment in the municipality of Nova Odessa, São Paulo, found average values of monthly pasture productivity of approximately 1450 Kg of dry matter / ha. Rosa and Sano (2013), in a study carried out in the Paranaíba River basin, in Minas Gerais state, indicated an average productivity of approximately 1000 kg of dry matter / ha / month. These differences may be related to several factors, such as soil fertility, water infiltration and storage capacity, and site-specific temperature and precipitation patterns. Figure 5 shows the potential stocking rate distribution in Goiás, according to the GPP model developed in this study, i.e., based on the use of 250 m MOD13Q1 NDVI images, local calibration parameters, and a high spatial resolution pasture map. It should be emphasized that the derivation of the potential bovine stocking rate is of fundamental importance for the implementation of public policies , aiming at increasing the support capacity of pasturelands .
Figure 5. Distribution of Animal Unit (AU) / Hectare for the State of Goiás, during 2015, based on the approach developed in this study.
Specifically, our bovine support capacity map showed a variation between <= 1.5 and 3.5 AU / ha, with average values close to 2.5 AU / ha, which is well above the current stocking rates found in most regions of Goiás, of about 1.2 AU / ha (EUCLIDES FILHO et al., 1997). The highest values of potential stocking rates were observed mainly in the central, southern and extreme northeast portions of the state, while the lowest values occurred predominantly in the northern and northwestern portions of the state (Araguaia river depression). However, in the northwestern portion, values of AU / ha between 2.5 and 3 were found especially in the municipalities of São Miguel do Araguaia, Novo Mundo, Novo Planalto, Novo Crixás, and Bonópolis (in the extreme northwest), which currently receive economic stimulus from the Programa ABC. The highest UA values were recorded in the northeastern region of the state, characterized by some terrain ondulation and
shallow soils. Thus, overestimated figures, as well as the need of additional investigations, can not be discarded. The accuracy of the land use map is of vital importance in the modeling of dry biomass, because if native vegetation areas are considered as pasture areas, these may have their values overestimated. However, this region of the Northeast of Goiás is a more conserved area due to the sloppy terrain, making it more difficult for the cattle to access, what can facilitate the accumulation of biomass in both native and cultivated pastures. In relation to the intensification potential of cattle raising for the State of Goiás (figure 06), it presented an average potential of 1.19 AU / ha, with the northeast, north and south regions having the highest values (1.5 to 3 AU / ha) of intensification potential. In other words, we could double the herd size in Goiás, without the need of converting additional Cerrado areas (i.e., avoiding new deforestations and soil degradation).
Figure 6. Distribution of the livestock intensification potential for the state of Goiás (referring to the year 2015).
The areas with low intensification potential may be related to the process of pasture degradation, a circumstance very common in the tropical pasture ecosystems (DIAS-FILHO, 2011). According to Dias-Filho (2011), major causes of pasture degradation, usually associated with a critical loss of soil fertility, include: a) indiscriminate and excessive fire use; b) pests and diseases; and c) climatic factors, when, in the event of prolonged droughts, can occur the emergence and / or development of invasive species, or, in case of excess humidity in the rainy season, can occur the proliferation of insect pests and diseases, as well as the loss of soil fertility through the acceleration of erosion and leaching processes. Another determining factor behind pasture degradation is the inadequate management, characterized by high stocking rates, grazing at non-optimal time intervals, no periodic replenishment of fertilizers in the soil, excessive use of fire, seeds of low cultural value and sowing in the improper season (DIASFILHO, 2011). Pasture degradation causes a substantial reduction in biomass production,
while greenhouse gas emissions tend to increase. On the other hand, pasture restoration can lead to both productivity and environmental gains. Crop-Livestock-Forest integrated systems have been one of the alternatives tested in the Cerrado, along with the correct number of cattle per hectare, a decision that this study can definitely contribute. 4. Final Considerations This research demonstrated the capability of using orbital remote sensing to estimate the livestock intensification potential in the State of Goiás, with reference to estimates of pasture productivity and bovine support capacity (i.e., Animal Unit / Hectare). Such estimates, based on the use of moderate spatial resolution satellite images and local biophysical parameters, are instrumental for the low cost and fast-track monitoring of the pasture areas in the Cerrado biome. In fact, the approaches developed in this study, as opposed to methods dependent on field biomass sampling,
can be easily extrapolated to the entire pasture areas in Brazil, Our results indicated an average potential stocking rate of about 2.08 AU / Ha, which is substantially higher than the current 1.2 AU / Ha encountered (on average) in the State of Goiás. Considering only this potential biomass productivity, the Goiás cattle herd could double without expanding existing pasture areas . In short, with the appropriate information (as the one derived in this study), proper management practices, and adequate public policies, cattle ranching in Goiás (and in Brazil) can be substantially improved (regarding productivity gains), while preventing new deforestations and increases in GHG emissions.
acuidade espacial dos mapeamentos da área de pastagem para o Brasil. In: XVIII Simpósio Brasileiro de Sensoriamento Remoto, 2017.
5. Acknowledgements
BUSTAMANTE, M.; NOBRE, C.; SMERALDI, R.; AGUIAR, A. P. D.; BARIONI, L. G.; FERREIRA., L. G.; LONGO, K.; MAY, P.; PINTO, A. S.; OMETTO, J. P. H. B. Estimating Greenhouse Gas Emissions from Cattle Raising in Brazil. Climatic Change, v. 115, p. 559-577, 2012.
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Declaration of Interest I declare that there are no conflicts of interest between the authors of the article entitled “Modeling Primary Gross Productivity in Tropical savanna for Livestock Intensification in Brazil” submitted to the Journal Remote Sensig Applications: Society and Environment.
Altamira, august 19, 2019 Gabriel Alves Veloso Author responsible for submission
Ethics in publishing I declare that the article entitled “Modeling Primary Gross Productivity in Tropical savanna for Livestock Intensification in Brazil” submitted to the Journal Remote Sensig Applications: Society and Environment, followed all ethical practices in its evelopment and writing.
Altamira, august 19, 2019 Gabriel Alves Veloso Author responsible for submission