Conveners
Digital & AI
- Enli Wang
- Tobias Reuter
- Introduction.
Cotton is Benin's leading export crop grown in different climatic zones. As a result of poor farming practices in agroecosystems leading to a decline in soil fertility, coupled with the phenomenon of climatic hazards, seed cotton yields of cultivated varieties are low. Given the context of soil degradation, agroecological practices are proposed as an alternative to...
Introduction: Adapting crops to climate will be challenged by shifting environments and increasing weather instability impacting both yield potential and stability. Exploring genotype x environment interactions (GEI) sources at large scale to develop outperforming and stable genotypes is an important step. Defining the Target Population of Environments (TPE) across the crop production area...
Introduction
Crop canopy reflectance is often used as a proxy for crop vitality. While it relatively easy to identify low vitality spots through vegetation indices (e.g. NDVI, WDVI, etc.) automating the identification of the causes of the low vitality spots remains an unsolved challenge. In fact factors that can cause a drop (or an increment) in vegetation indices, for example water and...
Introduction
To address agricultural challenges, engaging agroecological transition is crucial, necessitating a redesign strategy for productive and resilient biodiversity-based farming systems. However, implementing spatio-temporal design of diversified systems is complex due to the diverse factors that need to be considered, the large number of possible crops combinations in time and...
- Introduction:
Deep learning-based methods have shown success in predicting crop yield. However, it is still a challenge to train a deep learning model to effectively predict crop yield with only a few labeled observations, especially across small agricultural fields with high heterogeneity. Self-supervised learning (Liu et al., 2021) is a new technique addressing the challenge, but no...
- Introduction
Crop growth models can provide real-time forecasts of upcoming drought or nutrient deficiencies and can thus in principle be used to support decisions about irrigation and fertiliser application. To be useful for supporting in-season crop management, forecasts need to be (1) frequently updated and (2) sufficiently accurate. This second point is problematic in practice,...
