Mapping and explaining the productivity of Pinus radiata in New Zealand

Mapping Pinus radiata productivity for New Zealand not only provides useful information for forest owners, industry stakeholders and policy managers, but also enables current and future plantations to be visualised, quantified, and planned. Using an extensive set of permanent sample plots, split into fitting (n = 1,146) and validation (n = 618) datasets, models of P. radiata 300 Index (an index of volume mean annual increment) and Site Index (an index of height growth) were developed using a regression kriging technique. Spatial predictions were accurate and accounted for 61% and 70% of the variance for 300 Index and Site Index, respectively. Productivity predicted from these surfaces for the entire plantation estate averaged 27.4 m³ ha⁻¹ yr⁻¹ for the 300 Index and 30.4 m for Site Index. Surfaces showed wide regional variation in this productivity, which was attributable mainly to variation in air temperature and root-zone water storage from site to site

A soil-landscape model for southern Mahurangi Forest, Northland

Exotic plantation forestry has a productive area of about 75 000 ha in Northland (L. Cannon, personal communication). Forestry is thus an important land use of both economic and environmental significance in Northland as well as elsewhere in New Zealand. Therefore, it is of considerable importance that forestlands be managed sustainably by employing approaches such as site-specific management. The establishment of site-specific forest management practices requires information regarding the distribution of key soil properties (Turvey and Poutsma, 1980). Quantitative modelling to predict key soil properties of sustainable forestry from observable landscape features may be a cost-effective approach to mapping forestlands. We are investigating the efficacy of such an approach within Mahurangi Forest, Northland

A soil-landscape model for Mahurangi Forest, Northland, New Zealand

Exotic plantation forestry is an important land use of both economic and environmental significance in Northland and elsewhere in New Zealand. It is therefore of considerable importance that forestlands be managed sustainably by employing approaches such as site-specific management. The establishment of site-specific forest management practices requires information regarding the distribution of key soil properties (Turvey and Poutsma, 1980). Quantitative modelling to predict key soil properties from landscape features may be an effective approach to mapping forestlands. A study investigating the efficacy of such an approach is being conducted within Mahurangi Forest, Northland, New Zealand. As a pilot to the study, a detailed qualitative soil-landscape model was developed in order to gain a greater understanding of the soil-landscape relationships and soil pattern of the area. The qualitative soil-landscape model developed in the pilot study is presented here

Mapping the productivity of radiata pine

Forest owners, investors and policy makers all want to know the spread and productivity of New Zealand’s current and future radiata plantation. David Palmer, a geo-spatial analyst at Scion, has combined advanced statistical techniques with mapping technology to predict radiata 300 Index and Site Index for any location in New Zealand. The 300 Index is an index of volume mean annual increment, and the Site Index is for height and growth. The map of Site Index and 300 Index was built using growth measurement data from trees in 1,146 radiata pine permanent sample plots, planted between 1975 and 2003. The data was combined with a number of climate, land use, terrain and environmental variables to predict forest productivity under a range of conditions

Isotopic signals in an agricultural watershed suggest denitrification is locally intensive in riparian areas but extensive in upland soils

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sigler, W. A., Ewing, S. A., Wankel, S. D., Jones, C. A., Leuthold, S., Brookshire, E. N. J., & Payn, R. A. Isotopic signals in an agricultural watershed suggest denitrification is locally intensive in riparian areas but extensive in upland soils. Biogeochemistry, 158, (2022): 251–268, https://doi.org/10.1007/s10533-022-00898-9.Nitrogen loss from cultivated soils threatens the economic and environmental sustainability of agriculture. Nitrate (NO3−) derived from nitrification of nitrogen fertilizer and ammonified soil organic nitrogen may be lost from soils via denitrification, producing dinitrogen gas (N2) or the greenhouse gas nitrous oxide (N2O). Nitrate that accumulates in soils is also subject to leaching loss, which can degrade water quality and make NO3− available for downstream denitrification. Here we use patterns in the isotopic composition of NO3− observed from 2012 to 2017 to characterize N loss to denitrification within soils, groundwater, and stream riparian corridors of a non-irrigated agroecosystem in the northern Great Plains (Judith River Watershed, Montana, USA). We find evidence for denitrification across these domains, expressed as a positive linear relationship between δ15N and δ18O values of NO3−, as well as increasing δ15N values with decreasing NO3− concentration. In soils, isotopic evidence of denitrification was present during fallow periods (no crop growing), despite net accumulation of NO3− from the nitrification of ammonified soil organic nitrogen. We combine previous results for soil NO3− mass balance with δ15N mass balance to estimate denitrification rates in soil relative to groundwater and streams. Substantial denitrification from soils during fallow periods may be masked by nitrification of ammonified soil organic nitrogen, representing a hidden loss of soil organic nitrogen and an under-quantified flux of N to the atmosphere. Globally, cultivated land spends ca. 50% of time in a fallow condition; denitrification in fallow soils may be an overlooked but globally significant source of agricultural N2O emissions, which must be reduced along-side other emissions to meet Paris Agreement goals for slowing global temperature increase.National Institute of Food and Agriculture, 2011–51130-31121, S. A. Ewing, 2011, S. A. Ewing, 2016–67026-25067, S. A. Ewing, Montana State University Extension, Montana Fertilizer Advisory Committee, Montana Agricultural Experiment Station, Montana State University Vice President of Research, Montana State University College of Agriculture, Montana Institute on Ecosystems, NSF EPSCoR, OIA-1757351, S. A. Ewing, OIA-1443108, S. A. Ewing, EPS-110134, S. A. Ewing

Simulating the influences of groundwater on regional geomorphology using a distributed, dynamic, landscape evolution modelling platform

A dynamic landscape evolution modelling platform (CLiDE) is presented that allows a variety of Earth system interactions to be explored under differing environmental forcing factors. Representation of distributed surface and subsurface hydrology within CLiDE is suited to simulation at sub-annual to centennial time-scales. In this study the hydrological components of CLiDE are evaluated against analytical solutions and recorded datasets. The impact of differing groundwater regimes on sediment discharge is examined for a simple, idealised catchment, Sediment discharge is found to be a function of the evolving catchment morphology. Application of CLiDE to the upper Eden Valley catchment, UK, suggests the addition of baseflow-return from groundwater into the fluvial system modifies the total catchment sediment discharge and the spatio-temporal distribution of sediment fluxes during storm events. The occurrence of a storm following a period of appreciable antecedent rainfall is found to increase simulated sediment fluxes

Driving better programme investment and accelerating challenge impact through a prioritisation matrix of international and national perspectives

This report presents the first stage of an overview of international and national drivers which have the potential to affect land use change and/or practice. The report is structured as follows; Chapter 1 will give an introduction and is followed by the methodology for quantifying the importance of these drivers in Chapter 2. Collation and valuation of drivers are described in Chapter 3, followed by scenario analysis to explore different futures in Chapter 4. The report finishes with suggestions for future research in Chapter 5