Water availability is a major factor constraining humanity's ability to meet the future food and energy needs of a growing and increasingly affluent human population. Water plays an important role ...

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The Global Food-Energy-Water Nexus

Nanobiotechnology has the potential to enable smart plant sensors that communicate with and actuate electronic devices for improving plant productivity, optimize and automate water and agrochemical allocation, and enable high-throughput plant chemical phenotyping. Reducing crop loss due to environmental and pathogen-related stresses, improving resource use efficiency and selecting optimal plant traits are major challenges in plant agriculture industries worldwide. New technologies are required to accurately monitor, in real time and with high spatial and temporal resolution, plant physiological and developmental responses to their microenvironment. Nanomaterials are allowing the translation of plant chemical signals into digital information that can be monitored by standoff electronic devices. Herein, we discuss the design and interfacing of smart nanobiotechnology-based sensors that report plant signalling molecules associated with health status to agricultural and phenotyping devices via optical, wireless or electrical signals. We describe how nanomaterial-mediated delivery of genetically encoded sensors can act as tools for research and development of smart plant sensors. We assess performance parameters of smart nanobiotechnology-based sensors in plants (for example, resolution, sensitivity, accuracy and durability) including in vivo optical nanosensors and wearable nanoelectronic sensors. To conclude, we present an integrated and prospective vision on how nanotechnology could enable smart plant sensors that communicate with and actuate electronic devices for monitoring and optimizing individual plant productivity and resource use.

Nanobiotechnology approaches for engineering smart plant sensors

The Statistical DownScaling Model (SDSM) is a freely available tool that produces high resolution climate change scenarios. The first public version of the software was released in 2001 and since then there have been over 170 documented studies worldwide. This article recounts the underlining conceptual and technical evolution of SDSM, drawing upon independent assessments of model capabilities. These studies show that SDSM yields reliable estimates of extreme temperatures, seasonal precipitation totals, areal and inter-site precipitation behaviour. Frequency estimation of extreme precipitation amounts in dry seasons is less reliable. A meta-analysis of SDSM outputs shows a preponderance of research in Canada, China and the UK, whereas the United States and Australasia are under-represented. In line with the wider downscaling community, the most favoured sector of analysis is water and flood risk management which accounts for nearly half of all output; research in other sectors such as agriculture, built environment and human health is less prominent but growing. Over 50% of the studies are concerned with production of climate scenarios, comparison or technical refinement of downscaling methodologies. In contrast, there is relatively little evidence of application to adaptation planning and climate risk management. We assert that further attention to physically meaningful quantities such as wind speeds, wave heights, phenological and hazard metrics could improve uptake of downscaled products. Chronic uncertainty in boundary forcing continues to undermine confidence in downscaled scenarios so these tools are best used for sensitivity testing and adaptation options appraisal.

http://mastercomputer.persiangig.com/document/Paper_Download/Page-1/19.pdf

The statistical downscaling model: insights from one decade of application

Future technologies and systemic innovation are critical for the profound transformation the food system needs. These innovations range from food production, land use and emissions, all the way to improved diets and waste management. Here, we identify these technologies, assess their readiness and propose eight action points that could accelerate the transition towards a more sustainable food system. We argue that the speed of innovation could be significantly increased with the appropriate incentives, regulations and social licence. These, in turn, require constructive stakeholder dialogue and clear transition pathways.

https://www.nature.com/articles/s43016-020-0074-1.pdf?proof=t

Innovation can accelerate the transition towards a sustainable food system

Growing demand for agricultural commodities for food, fuel and other uses is expected to be met through an intensification of production on lands that are currently under cultivation. Intensification typically entails investments in modern technology — such as irrigation or fertilizers — and increases in cropping frequency in regions suitable for multiple growing seasons. Here we combine a process-based crop water model with maps of spatially interpolated yields for 14 major food crops to identify potential differences in food production and water use between current and optimized crop distributions. We find that the current distribution of crops around the world neither attains maximum production nor minimum water use. We identify possible alternative configurations of the agricultural landscape that, by reshaping the global distribution of crops within current rainfed and irrigated croplands based on total water consumption, would feed an additional 825 million people while reducing the consumptive use of rainwater and irrigation water by 14% and 12%, respectively. Such an optimization process does not entail a loss of crop diversity, cropland expansion or impacts on nutrient and feed availability. It also does not necessarily invoke massive investments in modern technology that in many regions would require a switch from smallholder farming to large-scale commercial agriculture with important impacts on rural livelihoods. The current distribution of crops around the world neither attains maximum production nor minimum water use, according to a crop water model and yield data. An optimized crop distribution could feed an additional 825 million people and substantially reduce water use.

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Increased food production and reduced water use through optimized crop distribution

Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.

/pdf/can-sub-saharan-africa-feed-itself-2mf0v74v1t.pdf

Can sub-Saharan Africa feed itself?

The Global Yield Gap Atlas project (GYGA - http://yieldgap.org) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et al. (2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers daily weather data for 12 locations in Kenya for the years 1998-2012. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate bias corrected weather data from a combination of observed data and other external weather data. The bias corrected weather data consist of daily TRMM rain data and NASA POWER Tmax, Tmin, and Tdew data. These data are corrected based on calibrations with short-term (<10 years) observed weather data.

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Kenya public weather processed by the Global Yield Gap Atlas project

This chapter evaluates methods to estimate terrestrial ecosystem carbon assimilation and to monitor it over time at both local and national scales. This study presents simulated models that rely on vegetation indices and cover from Landsat 7 ETM sensors and vegetation stress scalars of temperature and moisture generated from the Thornthwaite water balance model. Temperature and moisture are the main climatic variables that affect vegetation productivity in the context of climate change at the landscape level. The temperature stress scalar in this study was derived from the seasonal optimal temperature for plant production from 28 years of climate data, while water stress was estimated from monthly water deficits, based on comparison of observed moisture supply (precipitation) to potential evapotranspiration (PET) demand derived using the Thornthwaite and Mather method (1957). The Carnegie-Ames-Stanford approach (CASA) model was used to estimate monthly patterns of carbon fixation using 30 M resolution satellite remote sensing images of the surface vegetation characteristics and driven by spatially interpolated climate data. IPCC climate general circulation models (GCM) scenarios were downscaled using a statistical downscaling model (SDSM) to generate locally useful data for impact analysis. Different vegetation classes show significant levels of sensitivity to temperature and moisture variability depicted by the varied net primary productivity (NPP) levels. Indigenous vegetation is well adapted and generally exhibited high NPP around Mount Kenya. This method can be useful in monitoring and evaluating ecosystem function of carbon sequestration and in facilitating optimisation of reducing carbon emissions from deforestation and degradation (REDD) through vegetation selection in forestry programmes.

Ochieng Adimo

Papers

Can sub-Saharan Africa feed itself?

Kenya public weather processed by the Global Yield Gap Atlas project

Net Primary Productivity Response to Climate Change in the Mount Kenya Ecosystem