AMAZING- Advancing MAiZe INformation for Ghana

The agricultural sector is under increasing pressure from population growth, climate change and environmental issues such as soil erosion, drought, flooding and pesticide overuse. In China, which has 20% of the world's population and only 10% of the arable land and water resources, huge changes have been made in agricultural practice in recent years to maintain food security. In the North China Plain (NCP), 61% of China's winter wheat and 45% of its maize are grown, but this is dependent on irrigation, causing a 1 m annual drop in the water table. Since 1985, double cropping of winter wheat and maize has been used to increase supply but this exacerbates water issues even further. China is investing heavily in technology at farm, regional and global scales and remote sensing forms a vital part of that strategy, for monitoring crop health and production.

In Ghana, more than half of its labour force is engaged in agriculture, which contributes to 54% of its GDP and provides over 90% of the country's food needs. At the same time, Ghana's agriculture predominantly involves smallholders and relies heavily on rain-fed subsistence farming practices, resulting in food production being below its potential. Similar to many other countries in the region, smallholders are widely considered to be the most vulnerable component of Ghana's rural sector. The project will use earth observation (EO) data and crop modelling techniques to help Ghana to build a national crop monitoring capability, informed by state-of-the-art developments in EO and crop modelling. Among various types of crops, food-crop farming is vital to low-income households and particularly women and children relying on subsistence agriculture. Timely monitoring information will result in better informed farmers and extension workers, as well as government, which will lead to better evidence-based decision-making, more efficient farming management, and more sustainable development in the Ghanaian agricultural industry in the long run.

Field survey and census has been traditionally used in many countries for crop yield estimation, but these have gradually been replaced or supplemented in many countries by EO-based estimates. EO data can provide a frequent measurement of a number of crop relevant biophysical parameters at a high spatial resolution (10-20 m). Using these data to monitor croplands, however, is hampered by the inherent difference between the nature of EO measurements and the agronomic parameters of interest. An alternative but equally compelling line of research is that of physical-based crop growth models (CGMs), which predict the crop development as a function of meteorological drivers, soil properties, specific cultivar parameters and management practices. The predictions from these CGMs, however, are not capable of describing a particular field with detail due to insufficient information on management practices, microclimate and soil variations, pests, etc. One way to account for this local variability is to use satellite observations to "correct" the model trajectory. In these "data assimilation" (DA) systems, the model "learns" about the reality of the crop from the observations.

The UCL team and its collaborators have conducted a series of DA work in China and Ghana in recent years. From these pioneering works, a number of major research challenges have been conquered for implementing a DA-based crop monitoring system. This project will demonstrate the practical benefits of EO-enabled crop monitoring and yield prediction for sustainable agriculture, to continue and deepen our collaborative partnerships between the UK and China and extend its impact, as well as to develop a partnership with Ghana to adapt the approach to meet local needs and conditions. This project will also present an opportunity to train in-country specialists both in EO, physical modelling, data assimilation, as well as in standard geoprocessing techniques.

With increasingly limited water resources, wheat farming in the North China Plain, where 50% of
the national grain is produced, faces a dilemma of balancing local water safety with national food security. The outputs of the proposed research will feed directly into agricultural production
planning in China, through the Ministry of Agricultural & Rural Affairs (MARA), in order to help the Chinese stakeholders monitor water stress more effectively. We will continue to provide periodic monitoring products to provincial Departments of Agriculture and municipal and county-level Bureaus of Agriculture.

In Ghana, 13 million people are employed in the rural sector and many of them are smallholder farmers that still rely on subsistent rain-fed agriculture. The funded activities address a pressing need in Ghana to improve the levels of crop performance information for yield optimization and national production planning. Built upon a series of previous ODA projects, this proposed work will help Ghana in building its capacity in smallholder cropland monitoring, as an example to address global challenges in reducing poverty and gender inequality. The monitoring capacity will also make a stride in addressing sustainable farming in Ghana and further enable Ghana's ability to mitigate the impact of climate change on agriculture, thus enhancing food security and agricultural sustainability. Having timely and accurate growth information on their main food crop would significantly improve management practices and help to avoid loss for food-crop farmers that are dominated by females in Ghana. The project undertaken will contribute to agricultural production monitoring in Ghana and bring a series of tangible impacts at farm/local scale, benefit stakeholders at the national level, make impacts worldwide, and contribute to the global scientific communities.

Globally, the challenges facing agriculture in China and Ghana are shared by many countries, particularly in Africa. Having the FAO as a partner in this project allows the faster and more directed dissemination of the project outputs for other countries and help to build their national capacities in cropland monitoring. We will work closely with the FAO Regional Office in Accra, as well as at other levels, to achieve this. The goals of this project are closely aligned with the Digitalisation of Agriculture movement that remains a priority in African development, and also aligned with the e-Agriculture initiatives promoted by both Ghana's MoFA and the FAO.

Finally, the techniques and tools developed in the project will be disseminated within the scientific community to promote wider uptake. We will extend our jointly ran UK-China workshops and training series in quantitative remote sensing to the worldwide research community. We will engage Chinese and Ghanaian scientists with international efforts such as the GEOGLAM and JECAM networks. The outputs will also contribute to the newly initiated GEOGLAM activity on constructing the "Essential Agricultural Variables" (EAVs) to support food security and the UN Sustainable Development Goals.