1. Monitoring forage productivity and its sensitivity to environmental factors using multi-source remote-sensing data

This WP is developing remote sensing-based approaches for forage monitoring and biomass prediction at local and regional levels with the aim of improving access to information that can improve decision making and help increase productivity and competitiveness while minimising impacts on the environment. The project will outline how the development of a remote sensing-based decision support system, calibrated and validated with in situ measurements can provide this information on an operational basis to ultimately benefit farmers, agricultural scientists and policy-decision makers nationwide. Using information obtained through the integration of satellite and airborne technologies, future strategies can be implemented for increasing production and improving pasture management while maintaining ecosystem attributes and services across farms. Specifically, this WP aims to: i) define growth patterns for different types of forages using remotely sensed (UAV and satellite multispectral remote sensing) vegetation indices and radar backscatter measurements; ii) generate high-resolution maps of forage quality and productivity metrics across spatial scales; and iii) analyse the dynamics of forage quality and productivity metrics and their sensitivities to local environmental factors; The information derived from this WP will provide insights into livestock productivity in different types of intensive and extensive production systems, capable of driving near real-time operational decisions and redesigning farming practices.

2. Forage productivity linkages to animal nutrition and productivity

This WP will integrate outputs from WP1 with near-real-time animal liveweight measurements to provide insights into the link between forage productivity and animal productivity. The main aim of this WP is to describe the relationships between remotely sensed forage indices, observed diurnal patterns of pasture utilization and grazing behaviour, and measures of animal performance. Specifically, we will characterise: i) animal growth dynamics in relation to pasture availability and pasture quality as indicated by remote sensing, ii) relationship between inter-individual variation in growth dynamics of cattle on pasture and the remotely sensed indicators of pasture abundance and quality, iii) heterogeneity of grazing patterns in relation to actual and predicted potential stocking rates from remotely sensed observations, iv) efficiency of biomass conversion by divergent livestock genotypes.

Selected farms are being equipped with robotic systems for weighing cattle on a daily basis and telecoms infrastructure is being set up for real-time data capture and upload. Animals on these farms are being fitted with sensor devices that monitor physiological state, location and activity. These data will be associated to the pasture production (directly measured and remotely sensed) variables derived from WP1. Availability of forage resources (WP1) will be integrated into simple feed management models using the ‘feed wedge’ principle – conserving feed when in excess to make up for shortfalls at other times (i.e. during the dry season). New technologies for the conservation of forage resources as silage will be developed using crops grown in growth chambers at IBERS and conserved using lab-scale (kg quantities) silos. Mixtures of forages will be prepared to assess synergies in chemical composition of different plants on fermentation and resultant silage nutritional characteristics, together with the development of feeding guidelines to improve cattle nutrition for a range of similar agro-ecozones.

3. Estimating variability of soil-based GHG emission factors

This WP seeks to understand the source(s) of variability in estimating robust soil-based GHG emission factors for inventory purposes forming the basis for mitigation strategies and modelling. Specifically, we will focus on: substrate (N) supply to the soil surface, substrate supply in the soil, production of microbial inhibitors released to the soil either in root exudates, animal dung or urine, modification of the soil abiotic environment (e.g. pH, soil moisture) and the size and identity of the N2O-producing microbiome. Excreta produced by animals in WP 2 (different diets) will be used in this WP. Specific measurements will test the following hypotheses: (1) Plant canopies will intercept a proportion of urine N and prevent it reaching the soil, thus reducing the substrate for N2O-producing processes in the soil. Plants will differ in their capacity to intercept N and this will interact with the defoliation regime; (2) Plants are associated with differences in soil mineral (and perhaps organic) N availability after cattle excreta is applied and the N availability influences N2O production; (3) Different species of plant can influence pH and soil water availability in the soil around the roots. These changes alter N transformation processes and consequently N2O emissions; (4) Plants develop distinctive microbiomes that are responsible for differences in soil N cycling and N2O emissions from cattle excreta.

4. Socio-economic modelling of forages under different climate change and management scenarios

In this WP, we will conduct different economic analyses of the forage materials evaluated in WP1 and WP2, including quantitative analysis of risks under different scenarios of climate change and management practices (e.g. grazing patterns). This is done through a discounted free cash flow model for the estimation of different profitability indicators capable of measuring the viability of different materials, such as: internal rate of return (IRR), net present value (NPV), benefit/cost ratio (B/C) and payback period of the investment, as well as the probability of obtaining negative values in the mentioned indicators. The model will be based on information/estimations on animal response as well as implementation and management costs of typical farms in three of the departments the project is working (Valle del Cauca, Cauca and Meta). To include levels of risk and uncertainty and to consider different scenarios, a quantitative risk analysis will be carried out using a Monte Carlo simulation, which considers the range of possible values for the key variables (e.g., weight gain) and the probability of occurrence. The climate change scenarios will be constructed with information obtained from IDEAM (the Colombian Institute for Hydrology, Meteorology and Environmental Studies) on regional climate forecasts and animal response data obtained from the technical work in WP2. This WP will provide key information that will help to increase transparency in investment decision-making processes to the following actors:

  • Livestock producers who often do not have the capacity to do financial assessments on their own and risk much when investing large amounts of money (which they mostly have to borrow) for new technology/production systems.
  • Advisors and extension workers who assist the primary producers in their decision making processes when it comes to making recommendations on implementing technical and financial viable technologies.
  • Decision-makers, such as governmental bodies, when it comes to the design of sustainable intensification strategies at country or department level.

5. Transition, agricultural intensification and behaviours towards increased use of technology in current practices

This WP aims to identify, from the smallholders’ perspective, functionings, capabilities and conversion factors that shape their farming practices under changing climate conditions; along with the barriers they experience to use new technologies and the smallholders’ possibilities to adapt to climate change. It seeks to answer the following questions; i) what resources, opportunities and capacities do producers have available/deploy in trying to incorporate new technologies and adapting to climate change?; ii) what are the barriers producers encountered/identified when incorporating new technologies in the daily management of their farms?; iii) how do farmers’ own knowledge and practices around pasture and forage management map onto the kind of decisions (i.e. optimal use of fertilizer, monitoring of forage quality and increasing the sustainability of farming practices) that the technology hoped to be able to support?; iv) how climate change has shaped and challenged Colombian smallholders’ farming practices (pastures, dairy production, crops and cattle management) ?; v) what are the capabilities and functionings deployed and developed by smallholders to cope with climate change?; and vi) what are the opportunities encountered and barriers experienced by female farmers while adopting new technologies and technology informed farming practices within a climate change context?

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