Mapping the spatial and temporal stability of production in mixed farming systems: an index that integrates crop and pasture productivity to assist in the management of variability

McEntee, Peter John; Bennett, Sarita Jane; Belford, R. K.

doi:10.1007/s11119-019-09658-6

Cited by 12 publications

(9 citation statements)

References 65 publications

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“…Jaynes et al (2005) identified the variation in the association between soybean yield and terrain attributes as being caused by the annual seasonality of climatic conditions. Variability in rainfall during the growing seasons is commonly associated with variation in crop yield over the years in MZs, as shown in several studies (Farid et al, 2016;McEntee et al, 2020;Schepers et al, 2004). Therefore, although elevation is not associated with the yield of the studied crops in certain growing seasons, the farmer can guide their management in each MZ based on climate forecasts.…”

Section: Spatial Correlation Analysis and Selection Of Variablesmentioning

confidence: 99%

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Delineation of management zones in integrated crop–livestock systems

et al. 2021

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The lack of studies on spatial variability in integrated crop–livestock systems (ICLS) hinders understanding how to increase their efficiency by implementing precision agriculture (PA) practices. As such, little is known about how grain and forage crops interact and how to improve the decision‐making process on fertilization and forage management. One technique that can help manage such systems is the delineation of management zones (MZs), regions with similar yield potential and soil and topography characteristics. Thus, this paper assesses the spatial correlation between yield and potential factors affecting it, and identifies whether it is possible to establish MZs for field management of grain and forage crops in succession in ICLS. Bivariate Moran's index was used to identify the attributes most spatially correlated with the yields. Elevation, soil apparent electrical conductivity, and clay content were the most spatially correlated variables with soybean [Glycine max (L.) Merr.] yield, while soil organic matter content and elevation were the most spatially correlated with the forage yield. Spatial principal components analysis and fuzzy c‐means clustering algorithm were combined to delineate MZs for each crop. The MZs created for soybean were statistically different in grain yield, available phosphorus (P) in the 0‐to‐0.40‐m layer and pH in the 0‐to‐0.20‐m layer. The forage MZs showed significant differences in terms of available P in the 0‐to‐0.40‐m layer. We conclude that MZs for ICLS tends to be crop specific, demanding different MZs to characterize soybean and forage spatial variability.

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Section: Spatial Correlation Analysis and Selection Of Variablesmentioning

confidence: 99%

“…Recently, McEntee et al. (2020) stated that modeling this system is a complex task, but possible to be performed through stable and unstable regions in terms of soil fertility and crop yield by using vegetation indices, georeferenced soil data, and soil apparent electrical conductivity (ECa).…”

Section: Introductionmentioning

confidence: 99%

Delineation of management zones in integrated crop–livestock systems

et al. 2021

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“…Spatial heterogeneity in socio-economic and ecological conditions determines the pattern of multiple ESs and thus leads to the spatial differences in ES trade-offs and synergies (Bennett et al, 2009;Potschin and Haines-Young, 2011;Torralba et al, 2018). Understanding how ESs interact with each other in complex and changing socio-economic and ecological conditions has become one main hurdle to achieve accurate locationbased management of multiple ESs (Lee and Lautenbach, 2016;Rova et al, 2019;McEntee et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Exploring social-ecological impacts on trade-offs and synergies among ecosystem services

Wang

Peng

Luo

et al. 2022

Ecological Economics

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“…Understanding seasonal changes in pasture availability and nutrient content can enhance ruminant production systems and management [6]. However, relatively little is known about the nature and extent of spatial variability of pasture production in mixed farming systems [7], where the combination of spatial variability and temporal instability creates a certain level of unpredictability, making it difficult for farm managers to make decisions, namely in the timing and optimum grazing intensity [6] or animal supplementation [2]. Farmers and animal nutritionists require an accurate, precise, rapid, and cost-effective method of assessing the nutritive value of pastures and feeds [8].…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

et al. 2021

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Dryland pastures provide the basis for animal sustenance in extensive production systems in Iberian Peninsula. These systems have temporal and spatial variability of pasture quality resulting from the diversity of soil fertility and pasture floristic composition, the interaction with trees, animal grazing, and a Mediterranean climate characterized by accentuated seasonality and interannual irregularity. Grazing management decisions are dependent on assessing pasture availability and quality. Conventional analytical determination of crude protein (CP) and fiber (neutral detergent fiber, NDF) by reference laboratory methods require laborious and expensive procedures and, thus, do not meet the needs of the current animal production systems. The aim of this study was to evaluate two alternative approaches to estimate pasture CP and NDF, namely one based on near-infrared spectroscopy (NIRS) combined with multivariate data analysis and the other based on the Normalized Difference Vegetation Index (NDVI) measured in the field by a proximal active optical sensor (AOS). A total of 232 pasture samples were collected from January to June 2020 in eight fields. Of these, 96 samples were processed in fresh form using NIRS. All 232 samples were dried and subjected to reference laboratory and NIRS analysis. For NIRS, fresh and dry samples were split in two sets: a calibration set with half of the samples and an external validation set with the remaining half of the samples. The results of this study showed significant correlation between NIRS calibration models and reference methods for quantifying pasture quality parameters, with greater accuracy in dry samples (R2 = 0.936 and RPD = 4.01 for CP and R2 = 0.914 and RPD = 3.48 for NDF) than fresh samples (R2 = 0.702 and RPD = 1.88 for CP and R2 = 0.720 and RPD = 2.38 for NDF). The NDVI measured by the AOS shows a similar coefficient of determination to the NIRS approach with pasture fresh samples (R2 = 0.707 for CP and R2 = 0.648 for NDF). The results demonstrate the potential of these technologies for estimating CP and NDF in pastures, which can facilitate the farm manager’s decision making in terms of the dynamic management of animal grazing and supplementation needs.

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Mapping the spatial and temporal stability of production in mixed farming systems: an index that integrates crop and pasture productivity to assist in the management of variability

Cited by 12 publications

References 65 publications

Delineation of management zones in integrated crop–livestock systems

Delineation of management zones in integrated crop–livestock systems

Exploring social-ecological impacts on trade-offs and synergies among ecosystem services

Near-Infrared Spectroscopy (NIRS) and Optical Sensors for Estimating Protein and Fiber in Dryland Mediterranean Pastures

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