Optimized number of suction cups required to predict annual nitrate leaching under varying conditions in Denmark.

Nitrate concentrations in soil water leaving the root zone measured by suction cups combined with water transport modeling is a commonly used practice in Denmark for calculating nitrate leaching. Two suction cups installed in one plot giving one water sample and replicated four times, (eight total suction cups) to reduce variability between samples. For practical reasons, it would be beneficial to minimize the number of suction cups used yet maintain reliable predictions. To assess the variability in reducing suction replicates, this study analyzed data from five research sites across Denmark representing annual field nitrate leaching predictions for different combinations of soil, weather conditions, crops, N-fertilizer rates, and winter soil cover, covering a total of 173 annual nitrate leaching experiments. The analysis was conducted having different nitrate leaching predictions using different numbers of replicates of suction cup measurements. Linear regression was used to identify the different influences of leaching year (hydrological year), N rate applied, soil characteristics, and crop sequence on nitrate leaching. The analyses were set up on three 2-yr and two 3-yr field experiments in five different sites. Crop effects showed that cereals and winter cover sown in autumn 2017 had significantly more nitrate leaching than in 2015 and 2016 leaching years due to high precipitation rates in the autumn. Furthermore, decreasing the number of suction cup replicates from four (eight total) to three replicates (six total) did not have a significant effect on nitrate leaching prediction. In contrast, decreasing replicates from four to two (four total) and one (two total) replicates did show a significant difference. Therefore, using three replicates is a viable solution for future sampling strategies and a good trade-off between costs and accuracy.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

The compound effects of drought and high temperature stresses will be the main constraints on maize yield in Northeast China.

Compound climate extremes such as drought and high temperature have a greater impact on agricultural production than the individual extremes. An increasing frequency and intensity of the compound climate extremes has been observed and projected under climate change, yet partitioning the total impacts to individual ones on crop yield has not been well assessed. In this study, we assessed the compound and separate effects of drought and high temperature on maize yield under 9 climate-year types (CYTs) with different combinations of precipitation and temperature in Northeast China (NEC). The well-validated Agricultural Production Systems Simulator (APSIM) model was used to simulate the maize yield, driven by historical (1981-2017) and future climate data (2021-2060). The results show that CYTs of warm (warm-dry, warm-wet, warm) are prominent in the future under both Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. However, CYT of warm-wet increased mostly (11.5%) under RCP8.5, while warm-dry increased most (12.3%) under RCP4.5. The magnitude of maize yield loss caused by the compound of high temperature and drought (18.75%) is higher than the individual ones (drought 17.32% and high temperature 1.27%). There are variations in the effects of stresses on maize yield among CYTs and the yield reductions by the compound effects of drought and high temperature were warm-dry > warm > rainless > warm-wet > normal > cold-dry > cold > rainy > cold-wet. In addition, the yield loss was negatively correlated with Tmax and VPDmax but positively correlated with Prec. These findings imply the importance of fully considering the selection of heat and drought-resistant varieties and implementing supplementary irrigation for future climate mitigation strategies during maize production in NEC.

Refining the role of phenology in regulating gross ecosystem productivity across European peatlands.

The role of plant phenology as a regulator for gross ecosystem productivity (GEP) in peatlands is empirically not well constrained. This is because proxies to track vegetation development with daily coverage at the ecosystem scale have only recently become available and the lack of such data has hampered the disentangling of biotic and abiotic effects. This study aimed at unraveling the mechanisms that regulate the seasonal variation in GEP across a network of eight European peatlands. Therefore, we described phenology with canopy greenness derived from digital repeat photography and disentangled the effects of radiation, temperature and phenology on GEP with commonality analysis and structural equation modeling. The resulting relational network could not only delineate direct effects but also accounted for possible effect combinations such as interdependencies (mediation) and interactions (moderation). We found that peatland GEP was controlled by the same mechanisms across all sites: phenology constituted a key predictor for the seasonal variation in GEP and further acted as a distinct mediator for temperature and radiation effects on GEP. In particular, the effect of air temperature on GEP was fully mediated through phenology, implying that direct temperature effects representing the thermoregulation of photosynthesis were negligible. The tight coupling between temperature, phenology and GEP applied especially to high latitude and high altitude peatlands and during phenological transition phases. Our study highlights the importance of phenological effects when evaluating the future response of peatland GEP to climate change. Climate change will affect peatland GEP especially through changing temperature patterns during plant phenologically sensitive phases in high latitude and high altitude regions.