Simplified methods for on-farm prediction of yield potential of grazed lucerne crops in New Zealand

Moot, Derrick J.; Yang, Xiujuan; Ta, Hung; Brown, Hamish E.; Teixeira, Edmar; Sim, Richard E.; Mills, Annamaria

doi:10.1080/00288233.2021.1909078

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…Harvest management was set to cut pasture biomass the last day of each month (i.e., 12 times a year) and the residual green pasture biomass was set at 0.5 t DM ha −1 yr −1 . Such a value was considered to be sufficient to prevent the occurrence of an initial lag phase of regrowth (Moot et al, 2021). The model output assessed was annual herbage accumulation (t DM ha −1 yr −1 ).…”

Section: Methodsmentioning

confidence: 99%

Yield gaps of lucerne (Medicago sativa L.) in livestock systems of Argentina

Jáuregui

Ojeda²,

Berone

et al. 2022

Annals of Applied Biology

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Argentina grows the second-largest area of lucerne in the world. Despite its importance, a yield gap exists between potential and measured yields, but factors contributing to it are still unclear. This study aimed to identify management factors and research needs to reduce the lucerne yield gap to improve the livestock systems in this region. We used meteorological data coupled with lucerne crop modelling and measured yields from the National Lucerne Cultivar Evaluation Network (NLCEN) to quantify the lucerne yield gap in nine sites located within the Argentinian Pampas (between parallels 30-45 S and meridians 58-65 W) and three sites outside the Pampas. Specifically, we used the model developed by McCall & Bishop-Hurley (2003), adapted and calibrated for lucerne in Argentina by Berone et al. (2017) to estimate the potential yield (PY) for 12 locations (three irrigated and nine rainfed), and compared those results with measured yields from the NLCEN to calculate yield gaps. We found the average available photosynthetic active radiation (PAR) and temperatures were sufficient to achieve 21.5 ± 3.7 t dry matter (DM) ha À1 yr À1 under rainfed conditions (environments with mean annual rainfall from 400 to 1,200 mm). However, the average measured yield from the NLCEN was 16.8 ± 2.4 t DM ha À1 yr À1 (a 22% gap). Potential yields ranged between 10 and 25 t DM ha À1 yr À1 under rainfed conditions and between 25 and 39 t DM ha À1 yr À1 for irrigated crops. As latitude increased rainfed locations had lower yields, while irrigated locations had higher yields. Adding irrigation was predicted to increase yields to 35.4 ± 2.0 t DM ha À1 yr À1 (a 53% gap) in rainfed sites. For irrigated locations, the gap was smaller (27.3 ± 3.5 vs 32.4 ± 2.2 t DM ha À1 yr À1 for measured vs potential yield, respectively), and most likely linked to nutrient deficits. Also, current grazing management was estimated to achieve approximately 50% less grazing efficiency than optimal grazing management. Our results demonstrated that the livestock industry can potentially increase animal production under current environmental conditions. The four main adjustments to achieve this are increased use of irrigation, increased use of fertilisers, earlier commencement of grazing in spring and increased stocking rates.

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Section: Methodsmentioning

confidence: 99%

Yield gaps of lucerne (Medicago sativa L.) in livestock systems of Argentina

Jáuregui

Ojeda²,

Berone

et al. 2022

Annals of Applied Biology

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“…For lucerne that work has focussed on using 20 years of research to update the APSIM_Lucerne model with particular emphasis on understanding the impact of fall dormancy and grazing management on lucerne quality (Ta et al 2020) growth and development (Yang et al 2021(Yang et al , 2022. In addition, a simplified yield calculator based on thermal time has been developed to enable farmers in other regions to use local weather records to estimate yield (Moot et al 2022).…”

Section: Lucernementioning

confidence: 99%

“…2021, 2022). In addition, a simplified yield calculator based on thermal time has been developed to enable farmers in other regions to use local weather records to estimate yield (Moot et al . 2022).…”

Section: Introductionmentioning

confidence: 99%

A review of legume research and extension in New Zealand (1990–2022)

Moot

2022

Crop & Pasture Science

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Legumes have underpinned transformational change on New Zealand sheep and beef farms over the last 30 years. This was through an emphasis on ewe nutrition based on lucerne or red clover dominant pastures, and increased use of subterranean and white clovers on uncultivatable hill country. Pre- and post-weaning lamb growth rates have increased, and enabled earlier slaughter of heavier lambs. The farm systems results include greater numbers of hoggets mated, higher lambing percentages and greater ewe efficiency (kg lamb weaned/kg ewe mated). Extension packages to support legume use have compared growth rates of resident and legume-based pastures, economic analyses of successful farms and management packages for the most appropriate legume in different environments. Over the same period, the dairy industry rapidly expanded in cow numbers and area onto flat irrigated land on the Canterbury Plains. The nitrogen deficiency of perennial ryegrass was overcome by a linear increase in nitrogen fertiliser use. Environmental concerns from this intensification has led to a legislated nitrogen cap of 190 kg/ha.year. This, coupled with a recent trebling in urea price, has returned attention to increasing the white clover content of these pastures. Nitrogen applications can be minimised by using diverse pastures sown with a legume, herb and <8 kg/ha of perennial ryegrass. Work on other legumes, including annuals and those with condensed tannins, has to date failed to increase their use in most pastoral settings, with the exception of the perennial lupin which is adapted to high-aluminium soils in the South Island High Country.

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“…El corte de cuadrantes es laborioso y por eso se han desarrollado métodos y dispositivos para la estimación indirecta de la MF (4)(5)(6) . La altura del dosel de la pradera, medida con una regla graduada (sward stick) es útil para representar la MF, aunque la relación puede ser diferente en función de la composición botánica, densidad del dosel de la pradera y estación del año (7)(8)(9) . La altura del forraje comprimido medida con un plato de aluminio (rising plate meter), estima la MF considerando la densidad del dosel y es una práctica muy común a nivel de granja en países como Nueva Zelanda (2) .…”

Section: Introductionunclassified

“…La altura del forraje comprimido medida con un plato de aluminio (rising plate meter), estima la MF considerando la densidad del dosel y es una práctica muy común a nivel de granja en países como Nueva Zelanda (2) . La relación entre altura del dosel y MF en praderas de pasto ballico y trébol blanco es bien conocida y de aplicación rutinaria en Nueva Zelanda (10) ; para praderas con otras especies forrajeras como la alfalfa, se necesita más investigación para determinar la relación entre altura del dosel y MF (8) .…”

Section: Introductionunclassified

Estimación de masa de forraje en una pradera mixta por aprendizaje automatizado, datos del manejo de la pradera y meteorológicos satelitales

Guevara-Escobar

Cervantes-Jiménez

Lemus-Ramírez

et al. 2022

Rev. Mex. Cienc. Pecu.

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Medir la masa de forraje (MF) en la pradera, antes del pastoreo, es fundamental para determinar la asignación diaria de forraje en sistemas pastoriles de producción animal. La MF se estima por corte de forraje en áreas conocidas, utilizando ecuaciones alométricas, o con el uso de sensores de percepción remota (PR); sin embargo, la exactitud y practicidad de los distintos métodos para estimar la MF, es variable. El objetivo fue obtener modelos predictivos usando variables ambientales y del manejo de la pradera para predecir la MF. Se ajustaron modelos de regresión para estimar la MF con base en variables del manejo de la pradera (MP) o mediciones obtenidas por PR, como reflectancia, temperatura del aire y lluvia. Por tres años se estudió una pradera mixta pastoreada con bovinos productores de carne. Con 80 % de datos se modeló por mínimos cuadrados ordinarios (OLS) o por algoritmos de aprendizaje automatizado (ML). El 20 % restante de los datos se utilizó para validar los modelos usando el coeficiente de determinación y el sesgo promedio entre valores estimados y observados. El modelo base de estudio fue la relación entre la altura de la pradera antes del pastoreo y la MF de este modelo se ajustó usando OLS; la r2 fue 0.43. Cuando se ajustaron modelos que incluyeron variables del MP, la r2 fue 0.45 para OLS y 0.63 para ML. Al ajustar modelos con variables de MP y PR, la r2 fue 0.71 para OLS y 0.96 para ML. Los ensambles de modelos ajustados con ML redujeron el sesgo de estimados de MF de la pradera examinada. En general, los modelos de ML representaron mejor la relación entre altura de la pradera antes del pastoreo y MF que los de modelos de OLS, al ajustarlos con variables de manejo de la pradera y con información de PR. Los modelos de ML pueden usarse como herramienta para la toma de decisiones diaria en sistemas productivos pastoriles.

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Simplified methods for on-farm prediction of yield potential of grazed lucerne crops in New Zealand

Cited by 8 publications

References 38 publications

Yield gaps of lucerne (Medicago sativa L.) in livestock systems of Argentina

Yield gaps of lucerne (Medicago sativa L.) in livestock systems of Argentina

A review of legume research and extension in New Zealand (1990–2022)

Estimación de masa de forraje en una pradera mixta por aprendizaje automatizado, datos del manejo de la pradera y meteorológicos satelitales

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