Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems.

Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.

Extreme warm temperatures alter forest phenology and productivity in Europe.

Recent climate warming has shifted the timing of spring and autumn vegetation phenological events in the temperate and boreal forest ecosystems of Europe. In many areas spring phenological events start earlier and autumn events switch between earlier and later onset. Consequently, the length of growing season in mid and high latitudes of European forest is extended. However, the lagged effects (i.e. the impact of a warm spring or autumn on the subsequent phenological events) on vegetation phenology and productivity are less explored. In this study, we have (1) characterised extreme warm spring and extreme warm autumn events in Europe during 2003-2011, and (2) investigated if direct impact on forest phenology and productivity due to a specific warm event translated to a lagged effect in subsequent phenological events. We found that warmer events in spring occurred extensively in high latitude Europe producing a significant earlier onset of greening (OG) in broadleaf deciduous forest (BLDF) and mixed forest (MF). However, this earlier OG did not show any significant lagged effects on autumnal senescence. Needleleaf evergreen forest (NLEF), BLDF and MF showed a significantly delayed end of senescence (EOS) as a result of extreme warm autumn events; and in the following year's spring phenological events, OG started significantly earlier. Extreme warm spring events directly led to significant (p=0.0189) increases in the productivity of BLDF. In order to have a complete understanding of ecosystems response to warm temperature during key phenological events, particularly autumn events, the lagged effect on the next growing season should be considered.