NIRS for predicting Brown Swiss heifer diet composition on mixed pastures in the Amazon region

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Flores, Ysai Paucar, José A. Saucedo-Uriarte, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7608687/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study evaluated the use of near-infrared spectroscopy (NIRS) to predict the chemical composition of diets consumed by heifers grazing on mixed pastures. A total of 96 diet samples were collected from eight Brown Swiss heifers, dried at 60°C for 48 hours, and analysed for crude protein (CP), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), and in vitro dry matter digestibility (IVDMD). Samples were scanned using a Unity Scientific near-infrared spectrometer over the 1100–2500 nm wavelength range at 1 nm resolution. Prediction models were developed using partial least squares regression in UCAL software. Excellent calibration results were obtained for CP and NDF, with determination coefficients ( \(\:{R}_{c}^{2}\) ) of 0.99 and 0.94, respectively. Ash and ADF showed good predictive accuracy ( \(\:{R}_{c}^{2}\) = 0.85 and 0.86), while IVDMD predictions were moderate ( \(\:{R}_{c}^{2}\) = 0.74). These findings demonstrate that NIRS is a rapid, precise, and reliable tool for estimating key nutritional parameters in heifers’ mixed pasture diets, supporting its use for efficient forage quality monitoring. Calibration chemical composition cross validation mixed pastures NIRS Figures Figure 1 Introduction Cattle farming in the Amazon region of Peru plays a vital economic and social role, serving as a primary source of income for rural families and contributing significantly to food and nutritional security. Most livestock production in this region is based on extensive systems, where animals graze on natural and cultivated pastures. Therefore, understanding the chemical composition of bovine diets is essential for effective nutritional management and the formulation of feeding strategies that enhance productivity and reproductive performance. In addition, knowledge of both the nutritional composition of forage and the requirements of different animal categories is crucial to estimate carrying capacity and optimize feed plans, which facilitates the adjustment of diets to meet specific nutritional needs, thereby improving livestock performance and the efficient use of available forage resources. However, conventional analytical methods used for determining feed composition, such as wet chemistry, are costly, labor-intensive, generate polluting waste, and limit sample processing capacity in laboratories. Near-infrared spectroscopy (NIRS) offers a fast, non-destructive, chemical-free, and reliable alternative for assessing forage quality. Commonly predicted parameters using NIRS include crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and digestibility in pastures, hays, and concentrates (Buonaiuto et al., 2021 ); (MONRROY et al., 2017 ). While several studies have demonstrated the potential of NIRS for evaluating pasture and mixed hay rations (Buonaiuto et al., 2021 ), or analyzing fecal fiber content and digestibility (Simoni et al., 2024 ), limitations persist in predicting the nutritional composition of complex, multi-species forages typical of grazing systems, where animals selectively consume different plant species and parts (Norman et al., 2020; Parrini et al., 2019 ). In the northern Peruvian Amazon, typical grazing areas include mixtures of Trifolium repens , Lolium perenne , Lolium multiflorum , along with naturalized species such as Pennisetum clandestinum and Philoglossa mimuloides (Oliva et al., 2015 ). To date, no studies have applied NIRS to predict the chemical composition of such mixed diets under grazing conditions. Therefore, the objective of this study was to predict the chemical composition of the diet consumed by grazing Brown Swiss heifers using NIRS technology. Methodology Diet samples from eight Brown Swiss heifers (average 250 kg) were collected using manual simulation (Austin et al., 1983 ;Quispe et al., 2021 ) across six paddocks in Luya province, Peru (located in tropical low montane rainforest life zone), during both rainy and dry seasons. The heifers grazed a paddock after 60 days of rest. The paddocks were composed of twelve species in total, dominated by eight grasses ( Lolium multiflorum cv Cajamarquino, Dactylis glomerata , Holcus lanatus , and Penisetum clandestinum ) and two legumes ( Trifolium pratense and Trifolium repens ). In total, 96 samples (8 heifers × 6 paddocks × 2 seasons) were obtained. Samples were oven-dried at 60°C for 48 h and ground in a hammer mill. Chemical analyses included crude protein (CP) and ash (AOAC International (Association of Official Analytical Chemists), 2012 ), neutral detergent fiber (NDF) and acid detergent fiber (ADF) (Van Soest et al., 1991 ), and in vitro dry matter digestibility (IVDMD) following (Tilley & Terry, 1963). Near-infrared spectra were obtained using a SpectraStar 2500XL (Unity Scientific, USA), with a tungsten halogen lamp and InGaAs detector (1100–2500 nm range, 1 nm resolution). Scans were conducted in absorbance mode using a quartz cuvette (9.5 cm diameter, 5 cm height) filled with 16 ± 0.5 g of sample. Each sample was scanned three times, totaling 288 spectra. Partial least squares (PLS) regression models were developed using UCAL software (v3.0.4.9). Model performance was assessed using calibration ( \(\:{R}_{c}^{2}\) ), cross-validation ( \(\:{R}_{v}^{2}\) ), standard error of calibration (SEC), standard error of prediction (SEP), and cross-validation (SECV) (Shenk & Westerhaus, 2015 ). Prediction quality was interpreted as: weak (0.50–0.65), approximate (0.66–0.81), good (0.82–0.90), and excellent (> 0.91) (Molano et al., 2016 ). Results The chemical composition of the mixed pastures consumed by the heifers showed average values of 12.66% crude protein (CP), 9.67% total ash, 49.63% neutral detergent fiber (NDF), 30.86% acid detergent fiber (ADF), and 80.34% in vitro dry matter digestibility (IVDMD). Table 1 summarizes the calibration and cross-validation statistics for each nutritional parameter. Table 1 The accuracy of NIRS in predicting chemical composition of heifer's diet. Parameters Factor number Mathematical treatment 1 Dispersion correction \(\:{R}_{c}^{2}\) SEC \(\:{R}_{v}^{2}\) SECV SEP CP 12 1,4,4,1 SNV + D 0.99 0.32 0.95 0.50 0.28 Ash 7 1,8,8,1 SNV + D 0.85 0.34 0.83 0.36 0.34 NDF 8 1,8,8,1 SNV 0.94 1.22 0.88 1.40 1.02 ADF 8 1,8,8,1 D 0.86 1.08 0.78 1.15 0.95 IVDMD 6 1,4,4,1 D 0.74 2.41 0.58 2.63 2.05 1 The digits represent the order of the derivative, subtraction gap, firth smoothing and second smoothing. Crude protein and NDF showed excellent predictive performance, with coefficients of determination for calibration ( \(\:{R}_{c}^{2}\) ) of 0.99 and 0.94, and standard errors of calibration (SEC) of 0.32 and 1.22, respectively. Ash and ADF also showed good predictive accuracy, with \(\:{R}_{c}^{2}\) values of 0.85 and 0.86, and SEC values of 0.34 and 1.08. For IVDMD, an approximate prediction was obtained, with an \(\:{R}_{c}^{2}\) of 0.74 and a SEC of 2.41. Figure 1 presents the relationship between reference values (y-axis) and predicted values (x-axis) for each parameter. For CP (Fig. 1 a), most data points cluster closely around the line of identity, reflecting the highest \(\:{R}_{c}^{2}\) (0.99). Ash (b), NDF (c), and ADF (d) exhibit moderate dispersion, consistent with good predictive quality. In contrast, IVDMD (e) shows greater variability around the center line, aligning with the lower \(\:{R}_{c}^{2}\) of 0.74, indicating a more limited prediction capacity for this parameter. These results confirm the high predictive potential of the NIRS model, particularly for CP and NDF, while suggesting further refinement may be needed for parameters like IVDMD. Discussion The chemical composition of mixed pastures in this study aligned with previous reports for crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) in temperate mixed pastures (Lobos et al., 2013 ; Massignani et al., 2021 ). The in vitro dry matter digestibility (IVDMD) of 80.34% exceeded values reported by (Fernández-Cabanás et al., 2023 ) but remained within ranges documented by (Norman et al., 2020). Compositional variations are expected due to species diversity, including clover species that enhance nutritional quality (Azevedo Junior et al., 2012 ), along with differences in growth stage, soil conditions, climate, and grazing management (Catunda et al., 2022 ). NIRS predictive models demonstrated superior accuracy for CP and NDF ( \(\:{R}_{c}^{2}\) = 0.99 and 0.94, respectively) compared to previous studies ((Fekadu et al., 2010 ; Parrini et al., 2019 ). These high accuracies align with findings for mixed temperate and tropical forages (Massignani et al., 2021 ; Lobos et al., 2013 ). While ash and ADF predictions were satisfactory, IVDMD prediction accuracy was limited ( \(\:{R}_{c}^{2}\) = 0.74), suggesting the need for model refinement. The complexity of multi-species grazing mixtures likely affects calibration precision (Norman et al., 2020), whereas single-species studies typically achieve better results ((Pereira-Crespo et al., 2021 ; Zicarelli et al., 2023 ). NIRS technology effectively predicts CP and NDF in mixed pasture diets, with promising results for ash and ADF. Improving IVDMD prediction accuracy represents an important research priority for enhancing nutritional assessment of complex grazing systems. Declarations Ethical approval The experimental procedures involving animal data collection were approved by the Institutional Committee on Ethics for Scientific Research of the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (CIEI-N° 00165). Conflict of interest The authors declare no conflicts of interest for this article. Funding This work was supported by the Peruvian National Council of Science, Technology and Innovation - CONCYTEC under grant number 178-2015-FONDECYT. Authors contributions Flor L. Mejía: Formal analysis, investigation, methodology, project administration, writing – original draft. Ives Yoplac: Resources, software, writing - review & editing. Enrique R. Flores: Visualization, writing - review & editing. Ysai Paucar: Formal analysis, validation, writing - review & editing. José A. Saucedo-Uriarte: Formal analysis, methodology, visualization, writing - review & editing. William Bardales: Visualization, writing - review & editing. Hector V. Vasquez: Visualization, writing - review & editing. and Javier Ñaupari: Data curation, resources, supervision, visualization, writing - review & editing. All authors have approved the final version of the manuscript. Acknowledgement The authors thank Wilmer Bernal Mejía, owner of the Ecokuelap farm, for allowing us to carry out the work on his farm. Data availability Data sets generated during the current study are available from the corresponding author on reasonable request. 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Journal of Dairy Science, 74(10), 3583–3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2 Zicarelli, F., Sarubbi, F., Iommelli, P., Grossi, M., Lotito, D., Tudisco, R., Infascelli, F., Musco, N., & Lombardi, P. (2023). Nutritional Characteristics of Corn Silage Produced in Campania Region Estimated by Near Infrared Spectroscopy (NIRS). Agronomy, 13(3), 634. https://doi.org/10.3390/agronomy13030634 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 16 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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06:35:22","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73155,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7608687/v1/085328f9dcf20de5cca944fb.html"},{"id":92831409,"identity":"50a5baf6-f0f9-48f6-9f17-9a4a630bf4fe","added_by":"auto","created_at":"2025-10-06 06:35:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439083,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of predicted and reference values of CP, ash, NDF, ADF, and IVDMD.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7608687/v1/e95c85f72f26f7380908bca1.jpg"},{"id":92833244,"identity":"2efbb618-add8-4698-b254-26543fe38e52","added_by":"auto","created_at":"2025-10-06 07:07:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":847935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7608687/v1/54523bcb-bcb7-4d27-a6c0-44d45df64f9f.pdf"}],"financialInterests":"","formattedTitle":"NIRS for predicting Brown Swiss heifer diet composition on mixed pastures in the Amazon region","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCattle farming in the Amazon region of Peru plays a vital economic and social role, serving as a primary source of income for rural families and contributing significantly to food and nutritional security. Most livestock production in this region is based on extensive systems, where animals graze on natural and cultivated pastures. Therefore, understanding the chemical composition of bovine diets is essential for effective nutritional management and the formulation of feeding strategies that enhance productivity and reproductive performance. In addition, knowledge of both the nutritional composition of forage and the requirements of different animal categories is crucial to estimate carrying capacity and optimize feed plans, which facilitates the adjustment of diets to meet specific nutritional needs, thereby improving livestock performance and the efficient use of available forage resources. However, conventional analytical methods used for determining feed composition, such as wet chemistry, are costly, labor-intensive, generate polluting waste, and limit sample processing capacity in laboratories. Near-infrared spectroscopy (NIRS) offers a fast, non-destructive, chemical-free, and reliable alternative for assessing forage quality. Commonly predicted parameters using NIRS include crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), and digestibility in pastures, hays, and concentrates (Buonaiuto et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); (MONRROY et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While several studies have demonstrated the potential of NIRS for evaluating pasture and mixed hay rations (Buonaiuto et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), or analyzing fecal fiber content and digestibility (Simoni et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), limitations persist in predicting the nutritional composition of complex, multi-species forages typical of grazing systems, where animals selectively consume different plant species and parts (Norman et al., 2020; Parrini et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In the northern Peruvian Amazon, typical grazing areas include mixtures of \u003cem\u003eTrifolium repens\u003c/em\u003e, \u003cem\u003eLolium perenne\u003c/em\u003e, \u003cem\u003eLolium multiflorum\u003c/em\u003e, along with naturalized species such as \u003cem\u003ePennisetum clandestinum\u003c/em\u003e and \u003cem\u003ePhiloglossa mimuloides\u003c/em\u003e (Oliva et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To date, no studies have applied NIRS to predict the chemical composition of such mixed diets under grazing conditions. Therefore, the objective of this study was to predict the chemical composition of the diet consumed by grazing Brown Swiss heifers using NIRS technology.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eDiet samples from eight Brown Swiss heifers (average 250 kg) were collected using manual simulation (Austin et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1983\u003c/span\u003e;Quispe et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) across six paddocks in Luya province, Peru (located in tropical low montane rainforest life zone), during both rainy and dry seasons. The heifers grazed a paddock after 60 days of rest. The paddocks were composed of twelve species in total, dominated by eight grasses (\u003cem\u003eLolium multiflorum\u003c/em\u003e cv Cajamarquino, \u003cem\u003eDactylis glomerata\u003c/em\u003e, \u003cem\u003eHolcus lanatus\u003c/em\u003e, and \u003cem\u003ePenisetum clandestinum\u003c/em\u003e) and two legumes (\u003cem\u003eTrifolium pratense\u003c/em\u003e and \u003cem\u003eTrifolium repens\u003c/em\u003e). In total, 96 samples (8 heifers \u0026times; 6 paddocks \u0026times; 2 seasons) were obtained. Samples were oven-dried at 60\u0026deg;C for 48 h and ground in a hammer mill. Chemical analyses included crude protein (CP) and ash (AOAC International (Association of Official Analytical Chemists), \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), neutral detergent fiber (NDF) and acid detergent fiber (ADF) (Van Soest et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), and in vitro dry matter digestibility (IVDMD) following (Tilley \u0026amp; Terry, 1963). Near-infrared spectra were obtained using a SpectraStar 2500XL (Unity Scientific, USA), with a tungsten halogen lamp and InGaAs detector (1100\u0026ndash;2500 nm range, 1 nm resolution). Scans were conducted in absorbance mode using a quartz cuvette (9.5 cm diameter, 5 cm height) filled with 16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 g of sample. Each sample was scanned three times, totaling 288 spectra. Partial least squares (PLS) regression models were developed using UCAL software (v3.0.4.9). Model performance was assessed using calibration (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e), cross-validation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{v}^{2}\\)\u003c/span\u003e\u003c/span\u003e), standard error of calibration (SEC), standard error of prediction (SEP), and cross-validation (SECV) (Shenk \u0026amp; Westerhaus, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Prediction quality was interpreted as: weak (0.50\u0026ndash;0.65), approximate (0.66\u0026ndash;0.81), good (0.82\u0026ndash;0.90), and excellent (\u0026gt;\u0026thinsp;0.91) (Molano et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe chemical composition of the mixed pastures consumed by the heifers showed average values of 12.66% crude protein (CP), 9.67% total ash, 49.63% neutral detergent fiber (NDF), 30.86% acid detergent fiber (ADF), and 80.34% in vitro dry matter digestibility (IVDMD). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the calibration and cross-validation statistics for each nutritional parameter.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe accuracy of NIRS in predicting chemical composition of heifer's diet.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFactor number\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMathematical treatment\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDispersion correction\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSEC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{v}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSECV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSEP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,4,4,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSNV\u0026thinsp;+\u0026thinsp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,8,8,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSNV\u0026thinsp;+\u0026thinsp;D\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,8,8,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSNV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eADF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,8,8,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIVDMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1,4,4,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThe digits represent the order of the derivative, subtraction gap, firth smoothing and second smoothing.\u003c/p\u003e\u003cp\u003eCrude protein and NDF showed excellent predictive performance, with coefficients of determination for calibration (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e) of 0.99 and 0.94, and standard errors of calibration (SEC) of 0.32 and 1.22, respectively. Ash and ADF also showed good predictive accuracy, with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e values of 0.85 and 0.86, and SEC values of 0.34 and 1.08. For IVDMD, an approximate prediction was obtained, with an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e of 0.74 and a SEC of 2.41.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the relationship between reference values (y-axis) and predicted values (x-axis) for each parameter. For CP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), most data points cluster closely around the line of identity, reflecting the highest \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e (0.99). Ash (b), NDF (c), and ADF (d) exhibit moderate dispersion, consistent with good predictive quality. In contrast, IVDMD (e) shows greater variability around the center line, aligning with the lower \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e of 0.74, indicating a more limited prediction capacity for this parameter.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results confirm the high predictive potential of the NIRS model, particularly for CP and NDF, while suggesting further refinement may be needed for parameters like IVDMD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe chemical composition of mixed pastures in this study aligned with previous reports for crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) in temperate mixed pastures (Lobos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Massignani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The in vitro dry matter digestibility (IVDMD) of 80.34% exceeded values reported by (Fern\u0026aacute;ndez-Caban\u0026aacute;s et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) but remained within ranges documented by (Norman et al., 2020). Compositional variations are expected due to species diversity, including clover species that enhance nutritional quality (Azevedo Junior et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), along with differences in growth stage, soil conditions, climate, and grazing management (Catunda et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNIRS predictive models demonstrated superior accuracy for CP and NDF (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 0.99 and 0.94, respectively) compared to previous studies ((Fekadu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Parrini et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These high accuracies align with findings for mixed temperate and tropical forages (Massignani et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lobos et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While ash and ADF predictions were satisfactory, IVDMD prediction accuracy was limited (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 0.74), suggesting the need for model refinement. The complexity of multi-species grazing mixtures likely affects calibration precision (Norman et al., 2020), whereas single-species studies typically achieve better results ((Pereira-Crespo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zicarelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNIRS technology effectively predicts CP and NDF in mixed pasture diets, with promising results for ash and ADF. Improving IVDMD prediction accuracy represents an important research priority for enhancing nutritional assessment of complex grazing systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003cp\u003e The experimental procedures involving animal data collection were approved by the Institutional Committee on Ethics for Scientific Research of the Universidad Nacional Toribio Rodr\u0026iacute;guez de Mendoza de Amazonas (CIEI-N\u0026deg; 00165).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003cp\u003eThe authors declare no conflicts of interest for this article.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis work was supported by the Peruvian National Council of Science, Technology and Innovation - CONCYTEC under grant number 178-2015-FONDECYT.\u003c/p\u003e\u003ch2\u003eAuthors contributions\u003c/h2\u003e\u003cp\u003eFlor L. Mej\u0026iacute;a: Formal analysis, investigation, methodology, project administration, writing \u0026ndash; original draft. Ives Yoplac: Resources, software, writing - review \u0026amp; editing. Enrique R. Flores: Visualization, writing - review \u0026amp; editing. Ysai Paucar: Formal analysis, validation, writing - review \u0026amp; editing. Jos\u0026eacute; A. Saucedo-Uriarte: Formal analysis, methodology, visualization, writing - review \u0026amp; editing. William Bardales: Visualization, writing - review \u0026amp; editing. Hector V. Vasquez: Visualization, writing - review \u0026amp; editing. and Javier \u0026Ntilde;aupari: Data curation, resources, supervision, visualization, writing - review \u0026amp; editing. All authors have approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Wilmer Bernal Mej\u0026iacute;a, owner of the Ecokuelap farm, for allowing us to carry out the work on his farm.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eData sets generated during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAOAC International (Association of Official Analytical Chemists). (2012). Official Methods of Analysis of AOAC International (19th ed.). AOAC International.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAustin, D. D., Urness, P. J., \u0026amp; Fierro, L. C. (1983). Spring Livestock Grazing Affects Crested Wheatgrass Regrowth and Winter Use by Mule Deer. 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The Application of near Infrared Reflectance Spectroscopy (NIRS) to Forage Analysis (pp. 406\u0026ndash;449). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2134/1994.foragequality.c10\u003c/span\u003e\u003cspan address=\"10.2134/1994.foragequality.c10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimoni, M., Danese, T., Guerra, A., Goi, A., Pitino, R., De Marchi, M., Mitsiopoulou, C., Kyriakaki, P., Mavrommatis, A., Tsiplakou, E., \u0026amp; Righi, F. (2024). Evaluating NIRS for predicting faecal fibre, protein fractions and digestibility in dairy sheep and goats fed alfalfa hay and concentrate. 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Methods for Dietary Fiber, Neutral Detergent Fiber, and Nonstarch Polysaccharides in Relation to Animal Nutrition. Journal of Dairy Science, 74(10), 3583\u0026ndash;3597. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3168/jds.S0022-0302(91)78551-2\u003c/span\u003e\u003cspan address=\"10.3168/jds.S0022-0302(91)78551-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZicarelli, F., Sarubbi, F., Iommelli, P., Grossi, M., Lotito, D., Tudisco, R., Infascelli, F., Musco, N., \u0026amp; Lombardi, P. (2023). Nutritional Characteristics of Corn Silage Produced in Campania Region Estimated by Near Infrared Spectroscopy (NIRS). Agronomy, 13(3), 634. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agronomy13030634\u003c/span\u003e\u003cspan address=\"10.3390/agronomy13030634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Calibration, chemical composition, cross validation, mixed pastures, NIRS","lastPublishedDoi":"10.21203/rs.3.rs-7608687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7608687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study evaluated the use of near-infrared spectroscopy (NIRS) to predict the chemical composition of diets consumed by heifers grazing on mixed pastures. A total of 96 diet samples were collected from eight Brown Swiss heifers, dried at 60\u0026deg;C for 48 hours, and analysed for crude protein (CP), ash, neutral detergent fiber (NDF), acid detergent fiber (ADF), and in vitro dry matter digestibility (IVDMD). Samples were scanned using a Unity Scientific near-infrared spectrometer over the 1100\u0026ndash;2500 nm wavelength range at 1 nm resolution. Prediction models were developed using partial least squares regression in UCAL software. Excellent calibration results were obtained for CP and NDF, with determination coefficients (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e) of 0.99 and 0.94, respectively. Ash and ADF showed good predictive accuracy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 0.85 and 0.86), while IVDMD predictions were moderate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{c}^{2}\\)\u003c/span\u003e\u003c/span\u003e = 0.74). These findings demonstrate that NIRS is a rapid, precise, and reliable tool for estimating key nutritional parameters in heifers\u0026rsquo; mixed pasture diets, supporting its use for efficient forage quality monitoring.\u003c/p\u003e","manuscriptTitle":"NIRS for predicting Brown Swiss heifer diet composition on mixed pastures in the Amazon region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 06:35:17","doi":"10.21203/rs.3.rs-7608687/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-09-30T04:58:05+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T05:29:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T11:39:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-09-15T08:25:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c707a614-2d8b-4d66-8475-6d0ea8054d70","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T16:50:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 06:35:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7608687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7608687","identity":"rs-7608687","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}