Response of nitrate leaching to no-tillage is dependent on soil, climate, and management factors: A global meta-analysis.

No tillage (NT) has been proposed as a practice to reduce the adverse effects of tillage on contaminant (e.g., sediment and nutrient) losses to waterways. Nonetheless, previous reports on impacts of NT on nitrate ( NO 3 - ) leaching are inconsistent. A global meta-analysis was conducted to test the hypothesis that the response of NO 3 - leaching under NT, relative to tillage, is associated with tillage type (inversion vs non-inversion tillage), soil properties (e.g., soil organic carbon [SOC]), climate factors (i.e., water input), and management practices (e.g., NT duration and nitrogen fertilizer inputs). Overall, compared with all forms of tillage combined, NT had 4% and 14% greater area-scaled and yield-scaled NO 3 - leaching losses, respectively. The NO 3 - leaching under NT tended to be 7% greater than that of inversion tillage but comparable to non-inversion tillage. Greater NO 3 - leaching under NT, compared with inversion tillage, was most evident under short-duration NT (<5 years), where water inputs were low (<2 mm day-1 ), in medium texture and low SOC (<1%) soils, and at both higher (>200 kg ha-1 ) and lower (0-100 kg ha-1 ) rates of nitrogen addition. Of these, SOC was the most important factor affecting the risk of NO3 - leaching under NT compared with inversion tillage. Globally, on average, the greater amount of NO3 - leached under NT, compared with inversion tillage, was mainly attributed to corresponding increases in drainage. The percentage of global cropping land with lower risk of NO3 - leaching under NT, relative to inversion tillage, increased with NT duration from 3 years (31%) to 15 years (54%). This study highlighted that the benefits of NT adoption for mitigating NO 3 - leaching are most likely in long-term NT cropping systems on high-SOC soils.

Projected Wine Grape Cultivar Shifts Due to Climate Change in New Zealand.

Climate change has already been affecting the regional suitability of grapevines with significant advances in phenology being observed globally in the last few decades. This has significant implications for New Zealand, where the wine industry represents a major share of the horticultural industry revenue. We modeled key crop phenological stages to better understand temporal and spatial shifts in three important regions of New Zealand (Marlborough, Hawke's Bay, Central Otago) for three dominant cultivars (Merlot, Pinot noir, and Sauvignon blanc) and one potential new and later ripening cultivar (Grenache). Simulations show an overall advance in flowering, véraison, and sugar ripeness by mid-century with more pronounced advance by the end of the century. Results show the magnitude of changes depends on the combination of greenhouse gas emission pathway, grape cultivar, and region. By mid-century, in the Marlborough region for instance, the four cultivars would flower 3 to 7 days earlier and reach sugar ripeness 7 to 15 days earlier depending on the greenhouse gas emission pathway. For growers to maintain the same timing of key phenological stages would require shifting planting of cultivars to more Southern parts of the country or implement adaptation strategies. Results also show the compression of time between flowering and véraison for all three dominant cultivars is due to a proportionally greater advance in véraison, particularly for Merlot in the Hawke's Bay and Pinot noir in Central Otago. Cross-regional analysis also raises the likelihood of the different regional cultivars ripening within a smaller window of time, complicating harvesting schedules across the country. However, considering New Zealand primarily accommodates cool climate viticulture cultivars, our results suggest that late ripening cultivars or extended ripening window in cooler regions may be advantageous in the face of climate change. These insights can inform New Zealand winegrowers with climate change adaptation options for their cultivar choices.

Understanding spatial and temporal variability of N leaching reduction by winter cover crops under climate change.

Winter cover crops are sown in between main spring crops (e.g. cash and forage crops) to provide a range of benefits, including the reduction of nitrogen (N) leaching losses to groundwater. However, the extent by which winter cover crops will remain effective under future climate change is unclear. We assess variability and uncertainty of climate change effects on the reduction of N leaching by winter oat cover crops. Field data were collected to quantify ranges of cover crop above-ground biomass (7 to 10 t DM/ha) and N uptake (70 to 180 kg N/ha) under contrasting initial soil conditions. The data were also used to evaluate the APSIM-NextGen model (R2 from 62 to 96% and RMSEr from 7 to 50%), which was then applied to simulate cover crop and fallow conditions across four key agricultural locations in New Zealand, under baseline and future climate scenarios. Cover crops reduced N leaching risks for all location/scenario combinations but with large variability in space and time (e.g. 21 to 47% of fallow) depending on the climate change scenario. For instance, end-of-century estimates for northern (warmer) locations mostly showed non-significant effects of climate change on cover crop effectiveness and N leaching. In contrast for southern (colder) locations, there was a systematic increase in N leaching risks with climate change intensity despite a concomitant, but less than proportional, increase in cover crop effectiveness (up to ~5% of baseline) due to higher winter yields and N uptake. This implies that climate change may not only modify the geography of N leaching hotspots, but also the extent by which cover crops can locally reduce pollution risks, in some cases requiring complementary adaptive measures. The patchy- and threshold-nature of leaching events indicates that fine spatio-temporal resolutions are better suited to evaluate cover crop effectiveness under climate change.

Performance of Winter-Sown Cereal Catch Crops after Simulated Forage Crop Grazing in Southland, New Zealand.

(1) Background: Winter grazing of livestock poses significant environmental risks of nitrogen (N) leaching and sediment runoff. (2) Methods: A field study tested the effects of sowing catch crops of oats (Avena sativa L.), ryecorn (Secale cereale L.) or triticale (Triticosecale) in June and August (winter) in Southland, New Zealand (NZ), on the risk of N leaching losses from simulated N loads left after winter forage grazing. (3) Results: Catch crops took up 141-191 kg N ha-1 by green-chop silage maturity (approximately Zadoks growth stage 52; November/December). Importantly, early-sown catch crops were able to capture more N during the key leaching period from winter to mid-spring (77-106 kg N ha-1 cf. 27-31 kg N ha-1 for June and August treatments, respectively). At this time, ryecorn and triticale crops sown in June captured 20-29 kg ha-1 more N than June-sown oats (77 kg N ha-1). In October, early-sown catch crops reduced mineral N in the soil profile (0-45 cm depth) by 69-141 kg N ha-1 through the process of plant uptake. At green-chop silage maturity, catch crop yields ranged from 6.6 to 14.6 t DM ha-1. Highest yields and crop quality profiles (e.g., metabolizable energy, crude protein, soluble sugars and starch) were achieved by the oats, irrespective of the sowing date, indicating that trade-offs likely exist between environmental and productive performances of the catch crop species tested. (4) Conclusion: The catch crop of choice by farmers will depend on the desired end use for the crop, its place in the crop rotation and its potential for an environmental benefit.

Adapting crop rotations to climate change in regional impact modelling assessments.

The environmental and economic sustainability of future cropping systems depends on adaptation to climate change. Adaptation studies commonly rely on agricultural systems models to integrate multiple components of production systems such as crops, weather, soil and farmers' management decisions. Previous adaptation studies have mostly focused on isolated monocultures. However, in many agricultural regions worldwide, multi-crop rotations better represent local production systems. It is unclear how adaptation interventions influence crops grown in sequences. We develop a catchment-scale assessment to investigate the effects of tactical adaptations (choice of genotype and sowing date) on yield and underlying crop-soil factors of rotations. Based on locally surveyed data, a silage-maize followed by catch-crop-wheat rotation was simulated with the APSIM model for the RCP 8.5 emission scenario, two time periods (1985-2004 and 2080-2100) and six climate models across the Kaituna catchment in New Zealand. Results showed that direction and magnitude of climate change impacts, and the response to adaptation, varied spatially and were affected by rotation carryover effects due to agronomical (e.g. timing of sowing and harvesting) and soil (e.g. residual nitrogen, N) aspects. For example, by adapting maize to early-sowing dates under a warmer climate, there was an advance in catch crop establishment which enhanced residual soil N uptake. This dynamics, however, differed with local environment and choice of short- or long-cycle maize genotypes. Adaptation was insufficient to neutralize rotation yield losses in lowlands but consistently enhanced yield gains in highlands, where other constraints limited arable cropping. The positive responses to adaptation were mainly due to increases in solar radiation interception across the entire growth season. These results provide deeper insights on the dynamics of climate change impacts for crop rotation systems. Such knowledge can be used to develop improved regional impact assessments for situations where multi-crop rotations better represent predominant agricultural systems.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.