updated paper.bib and paper.md

Author: colmduff

joss_paper/paper/paper.bib

@@ -131,7 +131,7 @@ @misc{MACC:2023
 }
 
 @article{Leip:2008,
-  doi = {https://doi.org/10.5194/bg-5-73-2008},
+  doi = {10.5194/bg-5-73-2008},
   title = {Linking an economic model for European agriculture with a mechanistic model to estimate nitrogen and carbon losses from arable soils in Europe},
   author = {Leip, A. and Marchi, G. and Koeble, R. and Kempen, M. and Britz, W. and Li, C.},
   year = {2008},
@@ -140,7 +140,7 @@ @article{Leip:2008
 }
 
 @article{Havlík:2010,
-  doi = {https://doi.org/10.1016/j.enpol.2010.03.030},
+  doi = {10.1016/j.enpol.2010.03.030},
   title = {Global land-use implications of first and second generation biofuel targets},
   author = {Havlík, Petr and Schneider, Uwe A. and Schmid, Erwin and Böttcher, Hannes and Fritz, Steffen and Skalský, Rastislav and Aoki, Kentaro and De Cara, Stéphane and Kindermann, Georg and Kraxner, Florian and Leduc, Sylvain and McCallum, Ian and Mosnier, Aline and Sauer, Timm and Obersteiner, Michael},
   year = {2010},
@@ -149,7 +149,7 @@ @article{Havlík:2010
 }
 
 @article{Dietrich:2019,
-  doi = {https://doi.org/10.5194/gmd-12-1299-2019},
+  doi = {10.5194/gmd-12-1299-2019},
   title = {MAgPIE 4 – a modular open-source framework for modeling global land systems},
   author = {Dietrich, J. P. and Bodirsky, B. L. and Humpenöder, F. and Weindl, I. and Stevanović, M. and Karstens, K. and Kreidenweis, U. and Wang, X. and Mishra, A. and Klein, D. and Ambrósio, G. and Araujo, E. and Yalew, A. W. and Baumstark, L. and Wirth, S. and Giannousakis, A. and Beier, F. and Chen, D. M.-C. and Lotze-Campen, H. and Popp, A.},
   year = {2019},
@@ -158,7 +158,7 @@ @article{Dietrich:2019
 }
 
 @article{Beach:2012,
-  doi = {https://doi.org/10.1142/S2010007812500017},
+  doi = {10.1142/S2010007812500017},
   title = {Modeling Bioenergy, Land Use, And Ghg Emissions With Fasomghg: Model Overview And Analysis Of Storage Cost Implications},
   author = {Beach, Robert H. and Zhang, Yuquan W. and Mccarl, Bruce A.},
   year = {2012},

joss_paper/paper/paper.md

@@ -31,27 +31,27 @@ bibliography: paper.bib
 ---
 # Summary
 
-`OptiGob` is a Python-based tool designed to explore configurations of Ireland’s agriculture, forestry, and other land use (AFOLU) sectors. The main purpose is to assist users in the assessment of environmental and economic impact pathways, based on different land use transition pathways under varying assumptions about agriculture, afforestation, emissions abatement, and carbon dioxide removal (CDR) strategies. `OptiGob` combines data outputs from the GOBLIN Lite for agriculture and land use, from FERS-CBM for forestry, and from the LCAD2.0 for anaerobic digestion, to generate scenarios that respect biophysical constraints. `OptiGob` provides a flexible, customisable tool for researchers, policymakers, and educators to explore environmental and economic trade-offs associated with land use transition pathways.
+`OptiGob` is a Python-based tool designed to explore configurations of Ireland’s agriculture, forestry, and other land use (AFOLU) sectors. The main purpose is to assist users in the assessment of environmental and economic impact pathways, based on different land use transition pathways under varying assumptions about agriculture, afforestation, emissions abatement, and carbon dioxide removal (CDR) strategies. `OptiGob` combines data outputs from the `GOBLIN Lite` for agriculture and land use, from `FERS-CBM` for forestry, and from the `LCAD2.0` for anaerobic digestion, to generate scenarios that respect biophysical constraints. `OptiGob` provides a flexible, customisable tool for researchers, policymakers, and educators to explore environmental and economic trade-offs associated with land use transition pathways.
 
 # Statement of need
 
-Ireland's agricultural landscape is dominated by grassland, supporting extensive dairy and suckler beef production. Investment in Ireland's bio-economy requires substantial changes to the AFOLU sector. `OptiGob` allows users to manipulate critical AFOLU levers that determine transition pathways to explore their impacts. `OptiGob` utilises data from GOBLIN Lite [@Duffy:2024; @DuffyB:2022], FERS-CBM [@Black:2025; @Kurz:2008], and LCAD2.0 [@Martinez-Acre:2025] to calculate outputs from agriculture, forestry, and anaerobic digestion. This allows users to explore land use and livestock trade-offs, leveraging the outputs from the upstream models without the overhead of running upstream modelling chains. To the authors' knowledge, `OptiGob` represents the first attempt to do so in the Irish context. 
+Ireland's agricultural landscape is dominated by grassland, supporting extensive dairy and suckler beef production. Investment in Ireland's bio-economy requires substantial changes to the AFOLU sector. `OptiGob` allows users to manipulate critical AFOLU levers that determine transition pathways to explore their impacts. `OptiGob` utilises data from `GOBLIN Lite` [@Duffy:2024; @DuffyB:2022], `FERS-CBM` [@Black:2025; @Kurz:2008], and `LCAD2.0` [@Martinez-Acre:2025] to calculate outputs from agriculture, forestry, and anaerobic digestion. This allows users to explore land use and livestock trade-offs, leveraging the outputs from the upstream models without the overhead of running upstream modelling chains. To the authors' knowledge, `OptiGob` represents the first attempt to do so in the Irish context. 
 
-A range of integrated modelling tools exist to explore AFOLU interactions under climate and land constraints, including optimisation-based land-sector models (e.g., FASOM-GHG [@Beach:2012]) and regional or global partial-equilibrium frameworks such as CAPRI [@Leip:2008], GLOBIOM [@Havlík:2010], and MAgPIE [@Dietrich:2019]. These models typically operate at EU or global scales and solve equilibrium or dynamic land-allocation problems. `OptiGob` instead provides a lightweight, inventory-aligned optimisation layer tailored to rapid national policy exploration in Ireland.
+A range of integrated modelling tools exist to explore AFOLU interactions under climate and land constraints, including optimisation-based land-sector models (e.g., `FASOM-GHG` [@Beach:2012]) and regional or global partial-equilibrium frameworks such as `CAPRI` [@Leip:2008], `GLOBIOM` [@Havlík:2010], and `MAgPIE` [@Dietrich:2019]. These models typically operate at EU or global scales and solve equilibrium or dynamic land-allocation problems. `OptiGob` instead provides a lightweight, inventory-aligned optimisation layer tailored to rapid national policy exploration in Ireland.
 
 `OptiGob` estimates the net greenhouse gas (GHG) emissions from land use change and available emissions and land budget for grass-based livestock (Dairy and Suckler cow) production. `Pyomo` [@bynum2021pyomo; @hart2011pyomo] is used to optimise livestock populations, while respecting area commitment (afforestation, anaerobic digestion, BECCS (Bioenergy with Carbon Capture and Storage), protein crops) and emission (CO~2~e in the case of net-zero, or CH~4~ alongside net-zero CO~2~e for N~2~O and CO~2~ under a split gas target) constraints.
 
-The GOBLIN framework has been applied in recent studies of net-zero pathways for AFOLU [@Henn:2025; @Bishop:2024; @DuffyB:2022]. `OptiGob` builds upon this framework, providing a single-interface tool for exploring synergies and trade-offs across sectors.
+The `GOBLIN` framework has been applied in recent studies of net-zero pathways for AFOLU [@Henn:2025; @Bishop:2024; @DuffyB:2022]. `OptiGob` builds upon this framework, providing a single-interface tool for exploring synergies and trade-offs across sectors.
 
 # Model Overview
 
-Figure 1 illustrates the architecture of the `OptiGob` model. User-defined parameters are provided via JSON or YAML input files and parsed by a data manager, which supplies values to the relevant sub-modules. The `OptiGob` class orchestrates the overall model flow, coordinating modules for emissions, land area, and economic outcomes. Arrows represent data flow and dependencies, showing how these modules query sector-specific modules for forestry, livestock, bio-energy, other land uses, static agricultural (crops, sheep, pigs, poultry, and protein crops), and substitution effects. Scenario data are read from a pre-generated SQLite database derived from GOBLIN Lite, FERS-CBM, and LCAD2.0.
+Figure 1 illustrates the architecture of the `OptiGob` model. User-defined parameters are provided via JSON or YAML input files and parsed by a data manager, which supplies values to the relevant sub-modules. The `OptiGob` class orchestrates the overall model flow, coordinating modules for emissions, land area, and economic outcomes. Arrows represent data flow and dependencies, showing how these modules query sector-specific modules for forestry, livestock, bio-energy, other land uses, static agricultural (crops, sheep, pigs, poultry, and protein crops), and substitution effects. Scenario data are read from a pre-generated `SQLite` database derived from `GOBLIN Lite`, `FERS-CBM`, and `LCAD2.0`.
 
 ![OptiGob Architecture.\label{fig:Figure1}](../figures/flow.drawio.png)
 
 `OptiGob` explores Ireland’s AFOLU sector under alternative assumptions regarding productivity, emissions abatement, land use change, and carbon dioxide removal (CDR).
 
-The workflow first estimates CDR from forestry, harvested wood products, and BECCS to 2050. Fixed emissions from land use, crop production, pigs, poultry, and anaerobic digestion are then subtracted. The remaining emissions budget, defined by a GWP~100~ (100-year Global Warming Potential) net-zero target or a split-gas CH~4~ target, is used to optimise allowable cattle production via Pyomo, subject to land and emissions constraints. However, while the model optimises livestock populations (dairy and suckler beef) to meet constraints, it does not guarantee that net-zero or split-gas targets are achieved. Rather, it reports whether a given scenario is compliant.
+The workflow first estimates CDR from forestry, harvested wood products, and BECCS to 2050. Fixed emissions from land use, crop production, pigs, poultry, and anaerobic digestion are then subtracted. The remaining emissions budget, defined by a GWP~100~ (100-year Global Warming Potential) net-zero target or a split-gas CH~4~ target, is used to optimise allowable cattle production via `Pyomo`, subject to land and emissions constraints. However, while the model optimises livestock populations (dairy and suckler beef) to meet constraints, it does not guarantee that net-zero or split-gas targets are achieved. Rather, it reports whether a given scenario is compliant.
 
 Three levels of agricultural abatement are included: baseline (no additional measures), MACC-level (full implementation of measures in the Teagasc 2023 MACC [@MACC:2023]), and “frontier” (a high ambition pathway including grass-clover swards, methane inhibitors, anaerobic digestion, and manure management technologies).
 
@@ -61,7 +61,7 @@ Agricultural productivity is selectable at three tiers. More ambitious scenarios
 
 An illustrative example is provided for a 2050 climate neutrality target year, using a 2020 baseline and a split-gas approach. The CH~4~ emissions are reduced by 30% relative to the baseline, while CO~2~ and N~2~O are balanced under GWP~100~.
 
-Parameter selection reflects the 'frontier' (strong productivity increase) abatement path. The 10:1 dairy-to-beef ratio reflects a dairy-dominated pathway. A higher afforestation rate (16 kha per year), with a composition of 70:30 conifer to broadleaf split is applied. BECCS and bio-energy (anaerobic digestion and willow) are also included. Wetland restoration is assumed to be 90% of exploited peatland, and 50% of organic soils under grass rewetted. Pig and poultry output has also been increased by 20%.
+Parameter selection reflects the "frontier" (strong productivity increase) abatement path. The 10:1 dairy-to-beef ratio reflects a dairy-dominated pathway. A higher afforestation rate (16 kha per year), with a composition of 70:30 conifer to broadleaf split is applied. BECCS and bio-energy (anaerobic digestion and willow) are also included. Wetland restoration is assumed to be 90% of exploited peatland, and 50% of organic soils under grass rewetted. Pig and poultry output has also been increased by 20%.
 
 Figure 2 shows the emissions and removals by category for the baseline and transition scenario.