Modelling long-term changes in soil phosphorus and carbon under contrasting fertiliser and grazing management in New Zealand hill country

Soil carbon (C) stocks under permanent New Zealand pastures vary with slope and aspect due to differences in primary production, animal behaviour and nutrient return. An existing nutrient transfer model was extended using a web-based, general-purpose modelling tool to simulate long-term changes in soil phosphorus (P) and C in hill country under contrasting fertiliser and sheep stocking regimes. Three self-contained farmlets were examined: no P applied; 125 kg single superphosphate (SSP)/ha/year; and 375 kg SSP/ha/year, since 1980. The refined spatial model was able to simulate P and C distribution with varying slopes and aspects. For example, the mean annual changes in soil P and C were greater on low slopes and eastern aspects than on the other two slope and aspect positions, consistent with observed changes in these nutrients. However, the model overestimated changes in soil C, which highlighted both gaps in current knowledge and key factors influencing change in soil C stocks. Understanding the spatial patterns of soil C across the landscape will be critical in the design of soil C monitoring regimes, should soil C stocks be considered at a national level as a sink or source of CO2 emissions.Fil: Bilotto, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Vibart, Ronaldo. Agresearch Grasslands Research Centre; Nueva ZelandaFil: Mackay, Alec. Agresearch Grasslands Research Centre; Nueva ZelandaFil: Costall, Des. Agresearch Grasslands Research Centre; Nueva Zeland

Grass-Next – A Process-Based Model to Explore Nutrient and Carbon Dynamics in Topographically Complex Grazed Grasslands

Topographical features such as slope and aspect influence primary production, animal behavior and nutrient return to grazed grasslands. A new model was developed based on data collected during 40+ years of research in hill country landscapes, a long-term experiment on varying phosphorus (P) fertilizer rates and associated sheep stocking regimes. The Grass-NEXT model was able to simultaneously simulate total soil P (TSP), soil organic carbon (SOC) and total soil nitrogen (TSN) stock change and distribution in a topographically complex (hill country) landscape from 2003 to 2020. This model provided a basis for exploring, accounting, and reporting on changes in TSP, SOC and TSN stocks in response to current management practices (e.g., varying amounts of P fertilizer rates applied) in complex grazed systems. The model provided insights on both the combination of topographical features that provided the largest spatial and temporal variability across the landscape, and where more intensive sampling is required to detect a significant minimum change of 3% in total SOC stocks. Further work could improve the quantification of grazing activities and excreta deposition that would help to detect specific clusters of variation on topographical complex landscapes to facilitate soil sampling design

Discrepancies Between Observed and Predicted Climate-Driven Net Herbage Accumulation

The decline in net herbage accumulation (NHA) on the high phosphorus (P) fertilizer farmlet (HF) of a long-term P fertilizer and associated sheep grazing experiment in the last 25 years, aligns with the necessity to reduce the on-site nominal sheep stocking rates over the same period on this farmlet. This finding appears at odds with projected climate change driven modelling that forecast a largely positive outcome on pasture growth in summer moist environments. In this paper we explore the apparent discrepancies between the observed and predicted climate-driven NHA by using a climate-driven pasture growth module within a larger process-based model (AgPasture in APSIM) to simulate NHA, legume growth, nitrogen fixation and water balance across three slopes [Low (LS; 0°-12°), Medium (MS; 13°-25°) and High slope (HS;\u3e25°)] from 1980- 2021. To assess the ability of the model to capture the influence of spatiotemporal climate variables on pasture growth, the model output for 1972-1981 was compared with NHA measurements collected across the three slope classes for that same period. A good relationship was found between modelled and measured NHA across the three slopes classes giving confidence in the model’s ability to capture the influence of both spatial and temporal climate variation on plant growth. A comparison of the modelled NHA for the three slope classes during 1982-88 with 2012-2018 indicates a significant (p\u3c0.01) decline in NHA over time. There has been no clear trend in annual rainfall since 1982, however, mean daily maximum temperature has increased 1.5°C. The average modelled summer soil moisture deficit (January to March) has increased from -41mm between 1982- 1988 to -55 mm between 2012-2018. Our modelling work suggests that the summer soil moisture deficit and temperature stress are having a greater effect on NHA than the predicted benefits of higher [CO2] and winter and early spring temperatures, leading to long-term reductions in NHA, rather than an overall increase

Soil Carbon Stocks Are Stable under New Zealand Hill Country Pastures with Contrasting Phosphorus and Sheep Stocking Regimes

A temporal and spatial assessment is required to quantify the effects of nutrient inputs and varying grazing management regimes on soil organic carbon (SOC) stocks under grazed pastures in complex landscapes. We examined SOC stocks under permanent pastures in three farmlets under a range of different annual phosphorus (P) fertiliser and associated sheep stocking regimes. The farmlets examined had either no annual P applied (NF), 125 kg single superphosphate (SSP) ha-1 (LF), or 375 kg SSP ha-1 (HF) on an annual basis since 1980. Soils were sampled to three depths (0-75, 75-150, 150-300 mm) in 2003 and 2020, and to the two upper depths in 2014. Each farmlet included three slope classes [low slope (LS), medium slope (MS), high slope (HS)], on three different aspect locations [east (E), southwest (SW), northwest (NW)]. Although a trend (P = 0.07) was observed for greater SOC stocks in the upper depth of the HF farmlet (34.0 Mg C ha-1) compared with the other two farmlets (31.6 Mg C ha-1), this trend was discontinued in deeper layers. Accumulated SOC stocks (0-300 mm) were 111.1 (NF), 109.8 (LF) and 111.5 (HF) Mg C ha-1. Soil samples collected on HS resulted in higher soil bulk densities (BD) and carbon-to-nitrogen (C:N) ratios, and lower C concentration and SOC stocks, compared with samples collected on the other two slope classes. Soil samples collected on the NW-facing slopes resulted in higher BD, and lower C concentration and SOC stocks, compared with samples collected on the other two aspect locations. Under the current conditions, contrasting P fertiliser and sheep stocking regimes had minimal effects on SOC stocks. In contrast, topographic features had major effects on SOC stocks, and need to be considered in soil sampling protocols that monitor soil organic carbon stocks over space and time

Challenges and opportunities to capture dietary effects in on-farm greenhouse gas emissions models of ruminant systems

This paper reviews existing on-farm GHG accounting models for dairy cattle systems and their ability to capture the effect of dietary strategies in GHG abatement. The focus is on methane (CH4) emissions from enteric and manure (animal excreta) sources and nitrous oxide (N2O) emissions from animal excreta. We identified three generic modelling approaches, based on the degree to which models capture diet-related characteristics: from ‘none’ (Type 1) to ‘some’ by combining key diet parameters with emission factors (EF) (Type 2) to ‘many’ by using process-based modelling (Type 3). Most of the selected on-farm GHG models have adopted a Type 2 approach, but a few hybrid Type 2 / Type 3 approaches have been developed recently that combine empirical modelling (through the use of CH4 and/or N2O emission factors; EF) and process-based modelling (mostly through rumen and whole tract fermentation and digestion). Empirical models comprising key dietary inputs (i.e., dry matter intake and organic matter digestibility) can predict CH4 and N2O emissions with reasonable accuracy. However, the impact of GHG mitigation strategies often needs to be assessed in a more integrated way, and Type 1 and Type 2 models frequently lack the biological foundation to do this. Only Type 3 models represent underlying mechanisms such as ruminal and total-tract digestive processes and excreta composition that can capture dietary effects on GHG emissions in a more biological manner. Overall, the better a model can simulate rumen function, the greater the opportunity to include diet characteristics in addition to commonly used variables, and thus the greater the opportunity to capture dietary mitigation strategies. The value of capturing the effect of additional animal feed characteristics on the prediction of on-farm GHG emissions needs to be carefully balanced against gains in accuracy, the need for additional input and activity data, and the variability encountered on-farm

Backgrounding strategy effects on farm productivity, profitability and greenhouse gas emissions of cow-calf systems in the flooding pampas of Argentina

Beef grazing systems need to improve their environmental sustainability while increasing productivity to meet future demand. In a context of climate and prices variability, the main aim of our study was to explore the current trend in cow-calf operations of including backgrounding strategies on productivity, profitability and greenhouse gas (GHG) emissions in a representative beef cattle system from the Laprida Basin (Flooding Pampas, Argentina), applying an integrated assessment with modelling tools. The mean liveweight gain (LWG) of pure cow-calf systems was lower than systems that included backgrounding, it decreased as stocking rates (SR) increased, and it was increased when the stocker contribution (0.2 to 0.4 steer/cow rate), sales weights (steers 390 kg LW and heifers 320 kg LW) and supplementation level (>1% LW) were higher. Liveweight production and operating profits showed a curvilinear response to SR, reaching a plateau close to 0.5 cows ha−1. As expected, GHG emissions intensity (EI; kg CO2e kg−1 LW produced) was higher in pure cow-calf scenarios. If a grazing intensity (i.e. ratio between biomass removed by grazing and biomass available for grazing) beyond 0.6 was to be avoided to prevent long-term overgrazing and trade-offs among the variables assessed, the best option was to decrease SR to 0.45 cows ha−1. On such stocking rate, LWG was improved by 8% (±SD; ±3%), but LW production, operating profits, and GHG emissions intensity were reduced by 1% (±2%), 9% (±4%) and 10% (±1%), respectively, compared with 0.50 cows ha−1. The best risk-efficient combinations were depicted by backgrounding options and the variation of profit was mainly explained by prices variability (CV = 40 ± 3%) and, to a lesser extent by climate variability (CV = 11 ± 3%). Therefore, backgrounding strategies provide opportunities to farmers to increase farm productivity and profitability at the lowest risk for a given level of expected return, while reducing greenhouse gas emissions per unit of product.Fil: Bilotto, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; ArgentinaFil: Recavarren, Paulo Mario. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Balcarce. Agencia de Extension Rural Olavarria.; ArgentinaFil: Vibart, Ronaldo. Grasslands Research Centre; Nueva ZelandaFil: Machado, Claudio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigación Veterinaria de Tandil. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigación Veterinaria de Tandil. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Centro de Investigación Veterinaria de Tandil; Argentin

Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows

Dairy cows’ urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin’s concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems

Meta-Regression to Develop Predictive Equations for Urinary Nitrogen Excretion of Lactating Dairy Cows

Dairy cows’ urinary nitrogen (N) excretion (UN; g/d) represents a significant environmental concern due to their contribution to nitrate leaching, nitrous oxide (a potent greenhouse gas), and ammonia emissions (contributor to N deposition). The first objective of the current study was to determine the adequacy of existing models to predict UN from total mixed ration (TMR)-fed and fresh forage (FF)-fed cows. Next, we aimed to develop equations to predict UN based on animal factors [milk urea nitrogen (MUN; mg/dL) and body weight (BW, kg)] and to explore how these equations are improved when dietary factors, such as diet type, dry matter intake (DMI), or dietary characteristics [neutral detergent fiber (NDF) and crude protein (CP) content], are considered. A dataset was obtained from 51 published experiments composed of 174 treatment means. The whole dataset was used to evaluate the mean and linear biases of three existing equations including diet type as an interaction term; all models had significant linear and mean biases and two of the three models had poor predictive capabilities as indicated by their large relative prediction error (RPE; root mean square error of prediction as a percent of the observed mean). Next, the complete data set was split into training and test sets, which were used to develop and to evaluate new models, respectively. The first model included MUN and BW, and there was a significant interaction between diet type and the coefficients. This model had the worst 1:1 agreement [Lin’s concordance correlation coefficient (CCC) = 0.50] and largest RPE (24.7%). Models that included both animal and dietary factors performed the best, and when included in the model, the effect of diet type was no longer significant (p > 0.10). These models all had very good agreement (CCC ≥ 0.86) and relatively low RPE (≤13.1%). This meta-analysis developed precise and accurate equations to predict UN from dairy cows in both confined and pasture-based systems