Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry

Smethurst, P. J.; Huth, Neil; Masikati, Patricia; Sileshi, Gudeta W.; Akinnifesi, Festus K.; Wilson, J.; Sinclair, F.L.

doi:10.1016/j.agsy.2017.04.008

Cited by 66 publications

(61 citation statements)

References 35 publications

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“…This approach does not consider the possible impacts that tree-crop interactions may have (Luedeling et al, 2016), and some process-oriented models can address this by simulating the impacts of trees on the agroecosystem microclimate (e.g. solar interception, wind speed; Smethurst et al, 2017).…”

Section: Modelling To Oper Ationalis E Sc Smentioning

confidence: 99%

Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhouse gas removal technology

Sykes

MacLeod

Eory

et al. 2019

Global Change Biology

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To limit warming to well below 2°C, most scenario projections rely on greenhouse gas removal technologies (GGRTs); one such GGRT uses soil carbon sequestration (SCS) in agricultural land. In addition to their role in mitigating climate change, SCS practices play a role in delivering agroecosystem resilience, climate change adaptability and food security. Environmental heterogeneity and differences in agricultural practices challenge the practical implementation of SCS, and our analysis addresses the associated knowledge gap. Previous assessments have focused on global potentials, but there is a need among policymakers to operationalise SCS. Here, we assess a range of practices already proposed to deliver SCS, and distil these into a subset of specific measures. We provide a multidisciplinary summary of the barriers and potential incentives towards practical implementation of these measures. First, we identify specific practices with potential for both a positive impact on SCS at farm level and an uptake rate compatible with global impact. These focus on: (a) optimising crop primary productivity (e.g. nutrient optimisation, pH management, irrigation); (b) reducing soil disturbance and managing soil physical properties (e.g. improved rotations, minimum till); (c) minimising deliberate removal of C or lateral transport via erosion processes (e.g. support measures, bare fallow reduction); (d) addition of C produced outside the system (e.g. organic manure amendments, biochar addition); (e) provision of additional C inputs within the cropping system (e.g. agroforestry, cover cropping). We then consider economic and non‐cost barriers and incentives for land managers implementing these measures, along with the potential externalised impacts of implementation. This offers a framework and reference point for holistic assessment of the impacts of SCS. Finally, we summarise and discuss the ability of extant scientific approaches to quantify the technical potential and externalities of SCS measures, and the barriers and incentives to their implementation in global agricultural systems.

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Section: Modelling To Oper Ationalis E Sc Smentioning

confidence: 99%

Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhouse gas removal technology

Sykes

MacLeod

Eory

et al. 2019

Global Change Biology

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“…El matarratón, además de usarse en alimentación animal (Chaverri y Cicció, 2015), tiene propiedades melíferas (Fonte et al, 2013), se puede aprovechar su madera para leña, en sistemas silvopastoriles sirve para cercas vivas y/o sombra (Calle y Murgueitio, 2007), y en arreglos agroforestales se ha establecido en asocio con café o cacao (Hosseini et al, 2017). El matarratón ha sido establecido también, asociado a cultivos de maíz en modelos agroforestales en Kenia, África, para mejorar la fertilización del cultivo, atribuyéndosele además, la propiedad de favorecer el aprovechamiento del agua del suelo por parte del cultivo de maíz (Smethursta et al, 2017). En cuanto al totumo (C. cujete), en la costa Caribe colombiana, se ha utilizado en alimentación de rumiantes, a partir de la elaboración de ensilaje salino de la pulpa del fruto (Calle et al, 2011a;Flórez, 2012), y en sistemas silvopastoriles como arbusto para ramoneo (Rodríguez y Roncallo, 2013;Barragán, 2013).…”

Section: Usos De Las Especies Arbustivasunclassified

Arbustivas forrajeras: importancia en las ganaderías de trópico bajo Colombiano

2019

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Introducción. Las ganaderías de trópico bajo enfrentan el desafío de adaptar sus producciones a las consecuencias del cambio climático, que ha generado épocas secas prolongadas y escasez de alimento para los animales en estos periodos. Objetivo. El objetivo de esta revisión bibliográfica fue recopilar y analizar resultados de investigaciones sobre las especies forrajeras arbustivas: Tithonia diversifolia, Gliricidia sepium, Cratylia argentea y Crescentia cujete, como estrategia para mejorar la oferta nutricional en las ganaderías en trópico bajo. Desarrollo. En Colombia, las ganaderías de carne se localizan principalmente en trópico bajo, y suelen tener problemas de alimentación por la pobre calidad de las pasturas y su baja disponibilidad en la época seca. Los sistemas silvopastoriles (SSP) contribuyen a mejorar los indicadores productivos y reproductivos de los animales, ya que integran en diferentes arreglos y estratos, plantas leñosas perennes (árboles y/o arbustivas), leguminosas (rastreras, arbustivas) y pasturas, con una mayor oferta nutricional y forrajera que los sistemas convencionales. Las arbustivas T. diverfisolia, G. sepium, C. argentea, y C. cujete, por sus características multipropósito, producción de biomasa y adaptación a distintas condiciones climáticas y edáficas, han sido usadas en ramoneo, bancos forrajeros, cercas vivas y conservación de forrajes, p. ej. ensilaje. Conclusión. Por sus características, usos, producción de biomasa y perfil nutricional, sería pertinente evaluar el comportamiento de estas especies en estrategias de alimentación en ganaderías en condiciones de trópico bajo.

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“…Potato disease modelling, foresight, further model development Kroschel et al, 2013 [167]; Sporleder et al, 2013 [168]; Condori et al, 2014 [169]; Carli et al, 2014 [170]; Kleinwechter et al, 2016 [171]; Kroschel et al, 2017 [172]; Fleisher et al, 2017 [173]; Raymundo et al, 2017 [174]; Raymundo et al, 2017 [175]; Quiroz et al, 2017 [176]; Ramirez et al, 2017 [177]; Mujica et al, 2017 [178]; Scott and Kleinwechter, 2017 [179]; Petsakos et al, 2018 [180] AfricaRice: Model improvement, yield gap analysis, genotype × environment interactions, impact of climate change van Oort et al, 2014 [181]; van Oort et al, 2015 [182]; van Oort et al, 2015 [183]; Dingkuhn et al, 2015 [184]; van Oort et al, 2016 [185]; El-Namaky and van Oort, 2017 [186]; van Oort et al, 2017 [187]; Dingkuhn et al, 2017 [104,105]; van Oort and Zwart, 2018 [188]; van Oort, 2018 [189], Duku et al, 2018 [190] ICRAF: Agroforestry and intercropping modelling Africa Luedeling et al, 2014 [191]; Araya et al, 2015 [192]; Luedeling et al, 2016 [193]; Smethurst et al, 2017 [194], Masikati et al, 2017 [195] ILRI: crop-livestock-farm interactions Van Wijk et al, 2014 [196]; Herrero et al, 2014 [197] IITA: Modelling on Yams in West Africa Marcos et al, 2011 [198]; Cornet et al, 2015 [199]; Cornet et al, 2016 [200] ICARDA: Climate variability and change impact studies, foresight, conservation agriculture impact, genotype × environment interactions Sommer et al, 2013 [201]; Bobojonov and Aw-Hassan, 2014…”

Section: Cipmentioning

confidence: 99%

Role of Modelling in International Crop Research: Overview and Some Case Studies

et al. 2018

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Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.

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Accurate crop yield predictions from modelling tree-crop interactions in gliricidia-maize agroforestry

Cited by 66 publications

References 35 publications

Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhouse gas removal technology

Characterising the biophysical, economic and social impacts of soil carbon sequestration as a greenhouse gas removal technology

Arbustivas forrajeras: importancia en las ganaderías de trópico bajo Colombiano

Role of Modelling in International Crop Research: Overview and Some Case Studies

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