Subfield crop yields and temporal stability in thousands of US Midwest fields.

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha-1 for maize, 0.39 Mg ha-1 for cotton, 0.34 Mg ha-1 for soybean, and 0.59 Mg ha-1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11119-021-09810-1.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Drivers of within-field spatial and temporal variability of crop yield across the US Midwest.

Not all areas of a farmer's field are equal; some always produce more relative to the rest of the field, others always less, while still other areas fluctuate in their production capacity from one year to the next, depending on the interaction between climate, soil, topography and management. Understanding why the yield in certain portions of a field has a high variability over time-we call these areas unstable-is of paramount importance both from an economic and an environmental point of view, as it is through the better management of these areas that we can improve yields or reduce input costs and environmental impact. In this research, we analyzed data from 338 fields cultivated with maize, soybean, wheat and cotton in the US Midwest to understand how topographic attributes and rain affect yield stability over time. In addition to this high resolution yield monitor dataset, we used publicly available data on topography, rain and soil information to test the hypothesis that within-field areas characterized by a low topographic wetness index (proxy for areas with probability of lower water content) always perform poorly (low and stable yield) compared to the rest of the field because they are drier, and that areas of a field characterized by a mid-high wetness index (high and stable yield) always perform well relative to rest of the field because they have greater water availability to plants. The relative performance of areas of a field with a very high wetness index (e.g. depressions) strongly depends on rain patterns because they may be waterlogged in wet years, yielding less than the rest of the field, or wetter during dry years, yielding more than the rest of the field. We present three different observations from this dataset to support our hypothesis. First, we show that the average topographic wetness index in the different stability zones is lower in low and stable yield areas, high in high and stable yield areas and even higher in unstable yield areas (p < 0.05). Second, we show that in dry years (low precipitation at plant emergence or in July), unstable zones perform relatively better compared to the rest of the field. Third, we show that temporal yield variability is positively correlated (p < 0.05) with the probability of observing gleying processes associated with waterlogging for part of the year. These findings shed light on mechanisms underlying temporal variability of yield and can help guide management solutions to increase profit and improve environmental quality.

Transformation and stabilization of pyrogenic organic matter in a temperate forest field experiment.

Pyrogenic organic matter (PyOM) decomposes on centennial timescale in soils, but the processes regulating its decay are poorly understood. We conducted one of the first studies of PyOM and wood decomposition in a temperate forest using isotopically labeled organic substrate, and quantified microbial incorporation and physico-chemical transformations of PyOM in situ. Stable-isotope (¹³C and ¹5N) enriched PyOM and its precursor wood were added to the soil at 2 cm depth at ambient (N0) and increased (N+) levels of nitrogen fertilization. The carbon (C) and nitrogen (N) of added PyOM or wood were tracked through soil to 15 cm depth, in physically separated soil density fractions and in benzene polycarboxylic acids (BPCA) molecular markers. After 10 months in situ, more PyOM-derived C (>99% of initial 13C-PyOM) and N (90% of initial ¹5N-PyOM) was recovered than wood derived C (48% of 13C-wood) and N(89% under N0 and 48% under N+). PyOM-C and wood-C migrated at the rate of 126 mm yr ⁻¹ with 3-4% of PyOMC and 4-8% of wood-C recovered below the application depth. Most PyOM C was recovered in the free light fraction(fLF) (74%), with 20% in aggregate-occluded and 6% in mineral associated fractions ­ fractions that typically have much slower turnover times. In contrast, wood C was recovered mainly in occluded (33%) or dense fraction (27%).PyOM addition induced loss of native C from soil (priming effect), particularly in fLF (13%). The total BPCA-C content did not change but after 10 months the degree of aromatic condensation of PyOM decreased, as determined by relative contribution of benzene hexa-carboxylic acid (B6CA) to the total BPCA C. Soil microbial biomass assimilated 6-10% of C from the wood, while PyOM contributions was negligible (0.14­0.18%). The addition of N had no effect on the dynamics of PyOM while limited effect on wood.