Stage-specific genotype-by-environment interactions determine yield components in wheat.

In cereal crops, environmental fluctuations affect different physiological processes during various developmental phases associated with the formation of yield components. Because these effects are coupled with cultivar-specific phenology, studies investigating environmental responses in different cultivars can give contradictory results regarding key phases impacting yield performance. To dissect how genotype-by-environment interactions affect grain yield in winter wheat, we estimated the sensitivities of yield components to variation in global radiation, temperature and precipitation in 220 cultivars across 81 time-windows ranging from double ridge to seed desiccation. Environmental sensitivity responses were prominent in the short-term physiological subphases of spike and kernel development, causing phenologically dependent, stage-specific genotype-by-environment interactions. Here we reconcile contradicting findings from previous studies and show previously undetected effects; for example, the positive impact of global radiation on kernel weight during canopy senescence. This deep insight into the three-way interactions between phenology, yield formation and environmental fluctuations provides comprehensive new information for breeding and modelling cereal crops.

Straw amendment and nitrification inhibitor controlling N losses and immobilization in a soil cooling-warming experiment.

It is common practice in agriculture to apply high­carbon amendments, e.g. straw, or nitrification inhibitors (NI) to reduce soil nitrogen (N) losses. However, little is known on the combined effects of straw and NI and how seasonal soil temperature variations further affect N immobilization. We conducted a 113-day mesocosm experiment with different levels of 15N-fertilizer application (N0: control; N1: 125 kg N ha-1; N2: 250 kg N ha-1) in an agricultural soil, amended with either wheat straw, NI or a combination of both in order to investigate N retention and loss from soil after a cooling-warming phase simulating a seasonal temperature shift, i.e., 30 days cooling phase at 7 °C and 10 days warming phase at 21 °C. Subsequently, soils were planted with barley as phytometers to study 15N-transfer to a following crop. Straw addition significantly reduced soil N-losses due to microbial N immobilization. Although carbon added as straw led to increased N2O emissions at high N fertilization, this was partly counterbalanced by NI. Soil cooling-warming strongly increased ammonification (+77 %), while nitrification was suppressed, and straw-induced microbial N immobilization dominated. N immobilized after straw addition was mineralized at the end of the experiment as indicated by structural equation models. Re-mineralization in N2 was sufficient, but still suboptimal in N0 and N1 at critical times of early barley growth. N-use efficiency of the 15N tracer decreased with fertilization intensity from 50 % in N1 to 35 % in N2, and straw amendment reduced NUE to 25 % at both fertilization rates. Straw amendment was most powerful in reducing N-losses (-41 %), in particular under variable soil temperature conditions, but NI enforced its effects by reducing N2O emission (-40 %) in N2 treatment. Sufficient N-fertilization coupled with straw application is required to adjust the timely re-mineralization of N for subsequent crops.

Sentinel-2 Data for Precision Agriculture?-A UAV-Based Assessment.

An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

Decoupling of impact factors reveals the response of German winter wheat yields to climatic changes.

Yield development of agricultural crops over time is not merely the result of genetic and agronomic factors, but also the outcome of a complex interaction between climatic and site-specific soil conditions. However, the influence of past climatic changes on yield trends remains unclear, particularly under consideration of different soil conditions. In this study, we determine the effects of single agrometeorological factors on the evolution of German winter wheat yields between 1958 and 2015 from 298 published nitrogen (N)-fertilization experiments. For this purpose, we separate climatic from genetic and agronomic yield effects using linear mixed effect models and estimate the climatic influence based on a coefficient of determination for these models. We found earlier occurrence of wheat growth stages, and shortened development phases except for the phase of stem elongation. Agrometeorological factors are defined as climate covariates related to the growth of winter wheat. Our results indicate a general and strong effect of agroclimatic changes on yield development, in particular due to increasing mean temperatures and heat stress events during the grain-filling period. Except for heat stress days with more than 31°C, yields at sites with higher yield potential were less prone to adverse weather effects than at sites with lower yield potential. Our data furthermore reveal that a potential yield levelling, as found for many West-European countries, predominantly occurred at sites with relatively low yield potential and about one decade earlier (mid-1980s) compared to averaged yield data for the whole of Germany. Interestingly, effects related to high precipitation events were less relevant than temperature-related effects and became relevant particularly during the vegetative growth phase. Overall, this study emphasizes the sensitivity of yield productivity to past climatic conditions, under consideration of regional differences, and underlines the necessity of finding adaptation strategies for food production under ongoing and expected climate change.

The Contribution of Functional Traits to the Breeding Progress of Central-European Winter Wheat Under Differing Crop Management Intensities.

Wheat yields in many of the main producing European countries stagnate since about 20 years. Hence, it is of high interest, to analyze breeding progress in terms of yield and how associated traits changed. Therefore, a set of 42 cultivars (released between 1966 and 2012) was selected and yield as well as functional traits defined by the Monteith and Moss equation were evaluated under three levels of management intensity. The Monteith Moss equation thereby calculates grain yield as the product of incident photosynthetically active radiation, fraction of intercepted radiation, radiation use efficiency, and harvest index. The field trial was performed in a high yielding environment in Northern Germany in two seasons (2016-2017 and 2017-2018) with very contrasting rainfall rates. The three differing managements were: intensive (high N + pesticides), semi-intensive (high N - pesticides), and extensive (low N - pesticides). The results indicate that the stagnation of wheat yields in Central-Europe is not caused by a diminishing effect of breeding on yield potential. This equally applies to suboptimal growing conditions like extensified crop management and restricted water supply. Nearly all functional sub-traits showed a parallel progress but coefficients of determination of relationships between traits and year of variety release are decreasing along the hierarchy of yield formation. One exception is radiation interception which did not show a stable linear increase during breeding history. In recent years, biomass is getting more important in comparison to harvest index. Values of harvest index are slowly approaching theoretical maxima and correlations with grain yield are decreasing.

Breeding improves wheat productivity under contrasting agrochemical input levels.

The world cropping area for wheat exceeds that of any other crop, and high grain yields in intensive wheat cropping systems are essential for global food security. Breeding has raised yields dramatically in high-input production systems; however, selection under optimal growth conditions is widely believed to diminish the adaptive capacity of cultivars to less optimal cropping environments. Here, we demonstrate, in a large-scale study spanning five decades of wheat breeding progress in western Europe, where grain yields are among the highest worldwide, that breeding for high performance in fact enhances cultivar performance not only under optimal production conditions but also in production systems with reduced agrochemical inputs. New cultivars incrementally accumulated genetic variants conferring favourable effects on key yield parameters, disease resistance, nutrient use efficiency, photosynthetic efficiency and grain quality. Combining beneficial, genome-wide haplotypes could help breeders to more efficiently exploit available genetic variation, optimizing future yield potential in more sustainable production systems.

High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor.

INTRODUCTION: In recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season. MATERIALS AND METHODS: We therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well. RESULTS AND DISCUSSION: A linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R2: VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.

An alternative strategy of dismantling of the chloroplasts during leaf senescence observed in a high-yield variety of barley.

Changes in function and composition of the photosynthetic apparatus as well as the ultrastructure of chloroplasts in mesophyll cells were analyzed in flag leaves of the high-yield barley (Hordeum vulgare) variety cv. Lomerit during senescence under field conditions in two successive years. In contrast to previous results obtained with the elder variety cv. Carina photosystem II efficiency measured by F(v)/F(m) was found to be rather stable until a very late stage of senescence. Chlorophyll a fluorescence and P700 absorbance measurements revealed that the activities of the two photosystems declined in parallel. An increase in the chlorophyll a/b ratio at a late stage of senescence was observed to coincide with a decline in the level of the Lhcb1 apoprotein of the light harvesting complex (LHC) and the level of the corresponding transcript. Ultrastructural investigations revealed the presence of gerontoplasts that have long, single or pairwise thylakoids and lack large grana stacks. It is hypothesized that the early degradation of grana thylakoids harboring the major LHC reduced the risk of photoinhibition and might be causally related to the high yield of the barley variety cv. Lomerit.