Variation In Maize Yield Research Articles

Determining and managing maize yield gaps in Rwanda

Smallholder maize growers are experiencing significant yield gaps due to sub-optimal agricultural practices. Adequate agricultural inputs, particularly nutrient amendments and best management practices, are essential to reverse this trend. There is a need to understand the cause of variations in maize yield, provide reliable early estimates of yields, and make necessary recommendations for fertilizer applications. Maize yield prediction and estimates of yield gaps using objective and spatial analytical tools could provide accurate and objective information that underpin decision support. A study was conducted in Rwanda at Nyakiliba sector and Gashora sector located in Birunga and Central Bugesera agro-ecological zones, with the objectives of (1) determining factors influencing maize yield, (2) predicting maize yield (using the Normalized Difference Vegetation Index (NDVI) approach), and (3) assessing the maize yield gaps and the impact on food security. Maize grain yield was significantly higher at Nyakiliba (1.74 t ha−1) than at Gashora (0.6 t ha−1). NDVI values correlated positively with maize grain yield at both sites (R2 = 0.50 to 0.65) and soil fertility indicators (R2 = 0.55 to 0.70). Maize yield was highest at 40 kg P ha−1 and response to N fertilizer was adequately simulated at Nyakiliba (R2 = 0.85, maximum yield 3.3 t ha−1). Yield gap was 4.6 t ha−1 in Nyakiliba and 5.1 t ha−1 in Gashora. Soil variables were more important determinants of social class than family size. Knowledge that low nutrient inputs are a major cause of yield gaps in Rwanda should prioritize increasing the rate of fertilizer use in these agricultural systems.

Regional gap in maize production, climate and resource utilization in China

Maize is one of the three major grain crops in China; it is widely planted in Northeastern China (NEC), Northern China (NC) and Northwestern China (NWC). However, large regional variations in maize yield using the same cultivar and management measures occur due to climate diversity. It is important to clarify the differences in regional maize production and climate resources in order to improve resource utilization efficiency. Long-term field experiments with the same maize cultivar (i.e., Zhengdan 958) and management measures were carried out at 26 sites from 2007 to 2015 in the main corn-producing areas of China. Based on the field experiment data, we analyzed the regional differences in maize production according to yield, growth period, grain number per spike and 1000-grain weight. The climate resources and utilization efficiency in NEC, NC and NWC were then determined by combining daily temperature and solar radiation data. The results show that NWC had significantly greater maize yield compared to NEC and NC, by 49.5 % and 38.7 %, respectively. A non-significant difference in maize yield was found between NC and NEC, and similar results were also found for the 1000-grain weight. NWC experienced significantly greater solar radiation during the maize growth period; the value was 14.0 % and 19.6 % higher than in NEC and NC, respectively. In addition, thermal time was 5.1 % and 8.9 % higher than in NEC and NC, respectively. The radiation utilization efficiency (RUE) in the NWC was the highest, 14.5 % and 27.2 % higher than those in NC and NEC, respectively. Similar to RUE, the heat utilization efficiency (HUE) value in NWC was 27.4 % and 46.5 % higher than in NC and NEC, respectively. The yield of maize was positively correlated with the total radiation and thermal time during the growth period; however, regional radiation had a greater impact on maize production than thermal time.Overall, NWC showed higher maize yield, higher radiation and higher thermal time during the maize growth period as well as higher RUE and HUE compared to NC and NEC. Improving resource utilization efficiency using the theory and technology of quantitative matching of maize yield formation and resources in NC and NEC may be a way to reduce the regional yield gap in China.

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

AbstractWe used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.

Assessment of impacts of fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) on maize production in Ghana

Abstract Spodoptera frugiperda was considered an insect pest only in the Americas until its first report in African countries in 2016. In this study, farmers and agricultural officials in Ghana were interviewed on their perceptions and knowledge of the pest, on infestation and maize yield variations across years, and on management practices. Farms were inspected to determine the infestation level of 100 plants per hectare. Interviews revealed that farmers were familiar with the larval stages of this pest and noticed that the pest occurred throughout the year, but populations of S. frugiperda increased only during cropping seasons. Infestation levels reported by farmers in surveys were much lower in 2018 (30.38%) than in 2017 (80.92%). Farm inspections confirmed that infestation levels were much lower in 2018 (20.90%) than 2017 (73.70%). The belt formed by Guinea Savannah, Transitional Zone, and Semi-Deciduous Forest Agro-Ecological Zones (AEZs) recorded the highest infestations while the lowest were observed from the Sudan Savannah and Tropical Rain Forest AEZs. Insecticides were the most commonly used tactic to manage populations of this new pest. Maize yields increased across Ghana between 2013 and 2015 from 1.52 to 1.73 t/ha, decreased between 2015 and 2017 to 1.55 t/ha, and increased to 1.69 t/ha in 2018. The impact of fall armyworm injury to maize production is discussed.

Significance of disposable presowing irrigation in wheat in increasing water use efficiency and maintaining high yield under winter wheat-summer maize rotation in the North China Plain

Water scarcity in the North China Plain (NCP) is threatening irrigation of predominant winter wheat-summer maize rotation and thus rotation yield. To maximally reduce irrigation input while maintaining relatively high yield is urgently needed and an enduring challenge. Here we present an innovative practice that considers the water use of the entire rotation system, i.e., application of presowing irrigation to reach 85% of the field capacity for wheat (W0) compared with the common practice of applying twice more irrigation with each 750 m3 ha−1 during the wheat season (W2). After sowing maize, 750 m3 ha−1 irrigation was applied for both practices. The main hypothesis was that reducing wheat irrigation would lead to lower water content in the soil, which would in turn preserve more rain in the maize season, and that earlier maturity of wheat caused by reduced irrigation would advance earlier maize-sowing to utilize more radiation and thermal inputs resulting in higher maize yield which would compensate for decreased wheat yield. From 2012 to 2016, the 0–200 cm soil water contents, as well the yield and its components, were monitored at key growth stages of wheat and maize. There was consistently lower water content under W0 than under W2 from flowering to maturity of wheat making 4–5 days earlier maturity under the W0 practice, and led to a higher accumulated temperature of maize relative to the W2 practice. The grain yields of wheat, maize and rotation were 6694 to 9218 kg ha−1, 7284 to 12,843 kg ha−1, 16,336 to 20,605 kg ha−1, respectively. The temperature accumulated after silking well explained the interannual variation in maize yield. In contrast to temperature, rainfall during the maize season explained the yield difference between two practices. The maize yield was 6.4% higher under the W0 practice than under the W2 practice, in a year with sufficient rainfall, above 451 mm, and this trend was unpronounced under rare drought stress, below 250 mm of rainfall, leading to a 7.5% lower yield. The average yield of the four rotation rounds under W0 was equal to that under W2. Nevertheless, the W0 practice significantly increased the water use efficiency under rotation by 12.9%. On average, the irrigated amount in each rotation under W0 was significantly reduced by 1126 m3 ha−1 relative to W2. Therefore, the W0 practice is a simple and promising water management strategy for addressing shortages of water and labor while maintaining relatively high rotation yield in the NCP.

Yield and yield components of local cowpea (Vigna unguiculata L.) landraces grown in mixed culture with maize (Zea maysL.) in vertic cambisols in the Northern part of Tanzania

Smallholder farmers in the North eastern Tanzania practice cereal-legume mixed culture to manage risks of crop failure in sole crops. The production and productivity of cereal-legume intercrops may be influenced by the practice of cultivating two or more crops in the same space and time and the status of land races/cultivars involved as intercrops. Data on the effect of intercropping system on cowpea landraces with maize grown on Vertic Cambisols on yield and yield components are inadequate in the North eastern Tanzania although considerable knowledge has been accumulated on mixed culture system. A field experiment involving two cropping systems (sole crop and intercropping) was therefore conducted for two years (2017 and 2018) at Shirimatunda village, Kilimanjaro Region, North East Tanzania, to evaluate three local cowpea (Vigna unguiculata L.) landraces (i.e. landrace 1 (L1), landrace 2 (L2) and landrace 3 (L3)) grown in association with maize (Zea mays L). Intercropping significantly (P≤.05) decreased cowpea and maize yields. Performance of local cowpea landraces, however, did not show (P≤.05) any variation except for 100–seed weight where cowpea landrace 2 (L2) showed superiority compared with the other cowpea landraces. Although there was no significant variation in maize yield when intercropped with cowpea landraces, maize intercropped with cowpea landrace 1 gave numerically the highest grain yield. Regardless of cropping system and cowpea landraces, combined productivity of cowpea and maize increased in the intercropped plots as indicated by higher total land equivalent ratios (1.64 to 1.94). Highest LERT value was observed in the ‘Cowpea landrace 1’+Maize mixed culture. The observed total Land equivalent ratio (LER) values correspond to 48.25, 38.54 and 43.66 % of lands saved which could be used for other agricultural purposes. In these cropping systems, both cowpea landrace 1 & 3 and maize crop components were significantly complementary and most suitable in mixed culture as shown by competitive ratio (CR) values of 1.85 and 1.45 respectively.

Просторово-часові закономірності варіювання урожайності кукурудзи в Україні

Просторово-часові закономірності варіювання урожайності кукурудзи в Україні

Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa

Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world.

Assessing spatiotemporal variation of drought and its impact on maize yield in Northeast China

In the context of global climate change, drought has become an important factor that affects the maize yield in China. To analyse the impact of drought on maize yield loss in Northeast China in current and future climate scenarios, the Composite Meteorological Drought Index (CI) is introduced to reconstruct the following drought indicators: drought accumulative days (DAD), drought accumulative intensity (DAI), and consecutive drought days (CDD). These three drought indicators are used to describe the three-dimensional characteristics of drought in this study. Sen’s slope method and three-dimensional copula functions are adopted to analyse the variety of drought features, and Ensemble Empirical Mode Decomposition (EEMD) is used to analyse the variations in maize yield. A temporal assessment of the standardized yield residuals series (SYRS) of maize from 1961 to 2014 is conducted. A panel regression model is applied to demonstrate the drought impact on maize yield at various growth stages under the RCP4.5 scenario. The results show that the drought risk level for midwest Jilin Province, western Liaoning, and eastern Heilongjiang increase with global warming in the current scenario. The shorter three-dimensional joint return periods, 44–80yr, were mainly located in western Jilin Province, Liaodong Peninsula, and northwestern Liaoning. Eastern Heilongjiang has a slightly longer joint return period of 80–100yr. The SYRS shows a strong statistical correlation with drought indicator variations; drought-prone regions exhibit strong positive correlations. In comparison, excess precipitation regions show strong negative correlations with drought indicators in most growth stages. Drought indicators have a relatively strong association with SYRS at the milky–mature maize growth stage, and the occurrence of drought during this period primarily determines the maize yield changes in the future. Maize yield changes are −2.04%, −2.65% and −1.57% for Liaoning, Jilin, and Heilongjiang Province under the RCP4.5 scenario. These results can be used as a tool for early warning of drought risk to maize, and will accelerate the progress of drought disaster risk management.

Spatial controls of topsoil and subsoil organic carbon turnover under C3–C4 vegetation change

Soil organic carbon (SOC) is often heterogeneously distributed in arable fields and so is probably its turnover. We hypothesized that the spatial patterns of SOC turnover are controlled by basic soil properties like soil texture and the amount of rock fragments. To test this hypothesis, we cultivated maize as a C4 plant on a heterogeneous arable field (155×60m) that had formerly been solely cultivated with C3 crops, and monitored the incorporation of isotopically heavier maize-derived C into SOC by stable 13C isotope analyses. To obtain a homogeneous input of C4 biomass into the C3 soil across the field, we chopped the aboveground maize biomass after harvest in autumn and re-spread it uniformly over the field. Subsequently, the soil was grubbed and then ploughed in the next spring. In addition, we assessed the spatial patterns of SOC stocks, amount of rock fragments and texture, as well as potential soil organic matter (SOM) degradability by ex-situ soil respiration measurements. Heterogeneity of maize growth was monitored as a covariate using laser scanning and satellite images. After two years, maize C had substituted 7.4±3.2% of SOC in the topsoil (0–30cm) and 2.9±1.7% of SOC in the subsoil (30–50cm). Assuming that monoexponential decay mainly drove this SOC substitution, this resulted in mean residence times (MRT) of SOC in the range of 30±12years for the topsoil and of 87±45years for the subsoil, respectively. Variation in topsoil MRT was related to potential CO2 release during soil incubation (R2=0.51), but not to basic soil properties. In the subsoil, in contrast, the variation of maize C incorporation into the SOC pool was controlled by variations in maize yield (R2=0.44), which also exhibited a pronounced spatial variability (0.84 to 1.94kg dry biomass m−2), and which was negatively correlated with the amount of rock fragments (R2=0.48, p<0.001). We assume that heterogeneous input of belowground root biomass blurs the causal interactions between the spatial heterogeneity of soil properties and the related patterns of SOC turnover, and conclude that spatial patterns of SOC turnover are not easily predictable by standard soil analyses.

Satellite-based assessment of yield variation and its determinants in smallholder African systems.

The emergence of satellite sensors that can routinely observe millions of individual smallholder farms raises possibilities for monitoring and understanding agricultural productivity in many regions of the world. Here we demonstrate the potential to track smallholder maize yield variation in western Kenya, using a combination of 1-m Terra Bella imagery and intensive field sampling on thousands of fields over 2 y. We find that agreement between satellite-based and traditional field survey-based yield estimates depends significantly on the quality of the field-based measures, with agreement highest ([Formula: see text] up to 0.4) when using precise field measures of plot area and when using larger fields for which rounding errors are smaller. We further show that satellite-based measures are able to detect positive yield responses to fertilizer and hybrid seed inputs and that the inferred responses are statistically indistinguishable from estimates based on survey-based yields. These results suggest that high-resolution satellite imagery can be used to make predictions of smallholder agricultural productivity that are roughly as accurate as the survey-based measures traditionally used in research and policy applications, and they indicate a substantial near-term potential to quickly generate useful datasets on productivity in smallholder systems, even with minimal or no field training data. Such datasets could rapidly accelerate learning about which interventions in smallholder systems have the most positive impact, thus enabling more rapid transformation of rural livelihoods.

11-Year Rainfall and Maize Yield Variation (2003-2013) in Four Northern Districts of Malawi

&lt;p&gt;Rainfall in the maize cropping season (Oct-Apr) in the four northern districts of Malawi was examined in terms of seasonal fluctuation and spatial distribution, and data spanning 11 years were analyzed. Rainfall fluctuations in the 11-year period differed considerably among the four districts and the Extension Planning Areas (EPAs) showed high coefficients of variance (CVs) (16.9-93.7). The equation with the three-month rainfall (October, February, and April), i.e., Maize yield (kg/ha) in SH = 2.29 + 0.0042 × Oct rainfall – 0.0009 × Feb rainfall + 0.00045 × Apr rainfall (r&lt;sup&gt;2&lt;/sup&gt; = 0.41), better explained maize yield in the 2013/14 season than the equation with total rainfall in the cropping season. Rainfall accounted for more than 41% of the total variation in maize yields of smallholder farmers (SHs). Rainfall in April was the most critical factor influencing maize and other crop yields. After the Farm Input Subsidy Programme (FISP) was implemented in 2005/06, maize yield became more dependent on rainfall. CV was higher in maize than in groundnut and sweet potato, indicating that maize is susceptible to rainfall fluctuations, and groundnut and sweet potato should be incorporated in farming as a countermeasure against unpredictable rainfall.&lt;/p&gt;

Multi-site assessment of the effects of plastic-film mulch on dryland maize productivity in semiarid areas in China

Transparent plastic-film mulch combined with ridge-furrow technology is widely used to improve crop productivity in cold semiarid areas of China, but whether the technology has similar benefits in different climatic environments is not known. The present study was initiated to assess the variation of plastic-film mulch on maize (Zea mays L.) production in different semiarid environments. Four treatments: (i) plastic-film mulch, (ii) straw incorporation in soil without plastic-film mulch, (iii) straw incorporation plus plastic-film mulch, and (iv) no plastic-film mulch or straw incorporation (control) were imposed in ridge-furrow prepared fields at five sites along a hydrothermal gradient for up to 6 years. Compared to non-mulched plots, maize grain yield and aboveground biomass in mulched plots increased by 30–107% and 37–69%, respectively, across sites due to increased soil temperature and moisture principally during early growth. Plastic mulch increased grain yield to a greater extent at sites with higher hydrothermal limitations. Plastic mulch increased the harvest index of maize at cooler sites, but showed a decrease in harvest index at warmer sites, compared with no mulch. The mulch decreased the year-to-year variation in maize yield at the more hydrothermally-limited sites, but increased the variation at less hydrothermally-limited sites. Compared to no straw incorporation, straw incorporation increased grain yield and aboveground biomass by 7–12% and 8–12%, respectively, across the five sites. The present study suggests that the benefits of plastic-film mulch on maize grain yield and its stability varied significantly with local climatic conditions and that plastic-film mulch can be preferentially applied for maize production where both insufficient rainfall and low spring temperatures are limiting factors.

Maize Grain Yield and Dry Matter Production Responses to Variations in Weather Conditions

Variations in weather conditions could alter maize (Zea mays L.) growth and development. This study was conducted to determine the eco‐physiological determinants of variations in maize yield with weather conditions, and the relationship between grain yield, dry matter production, and climatic factors. Eight sowing dates were set at 15‐ to 20‐d intervals from mid‐March to mid‐July during 2012 and 2013 in the Huang‐Huai‐Hai region of China. When the sowing date was delayed, the yield increased initially and later declined, and the greatest yield was obtained at 12 June (SD6) sowing date for both years. The increased yield for SD6 was mainly attributed to the 1000‐kernel weight and post‐silking dry matter production, which were mainly influenced by the post‐silking plant growth rate. Variations in temperature and radiation were the primary factors that influenced the post‐silking dry matter production of maize, and eventually influenced grain yield. High temperatures (daily maximum temperature [Tmax] &gt; 28.1°C) during post‐silking under early sowing conditions and low temperatures (daily minimum temperature [Tmin] &lt; 17.7°C) under late sowing conditions combined with low radiation (accumulated radiation [Ra] &lt; 1 005.4 MJ m2) decreased the post‐silking plant growth rate, thereby decreasing the dry matter production and grain yield. Therefore, when the sowing was done from 25 May to 27 June, the relatively higher maize yield would be obtained. We conclude that variations in weather conditions (temperature and radiation) from silking to maturity significantly affect the plant growth rate of maize, influence post‐silking dry matter production, and grain yield.

Assessment of Maize Yield Variations Due to Climatic Variables of Rainfall and Temperature

The purpose of this study was to provide a better understanding of the maize yield variations as a function of climate in selected districts in Zambia. The specific objectives were: (i) to explore geographical patterns of the maize yield variations (ii) to investigate the possible relation between maize yield and climatic variables of rainfall and temperature. Data on maize yield was collected from Central Statistical Office while that for rainfall and temperature was collected from Zambia Meteorological Department. A mapped distribution of maize yield was produced to visualize the spatial pattern of maize yield across the selected districts. The strength and direction of the relationship between maize yield and rainfall and temperature was determined using correlations implemented in SPSS. Multiple regressions with ordinary least squares regression was used to fit models of how much variation in maize yield was explained by climatic variables. Results indicated that for one district (Nyimba), a significant (p = 0.05) explanation of variations in maize yield was attributed to levels of minimum and maximum temperature and amount of seasonal rainfall with 51.9% of the variation explained. However, the variation in maize yield that was explained by rainfall and temperature was not significant for the rest of the districts considered in the study.

Using satellite remote sensing to understand maize yield gaps in the North China Plain

Substantial gaps have been reported between the average farmer’s maize yield and yield potential in China, especially the North China Plain (NCP). This maize yield gap as identified by previous studies indicates large opportunities for raising yield by improving agronomy. Agronomic factors are either transient or persistent. Transient factors, which explain yield differences depending on unpredictable weather conditions, can have significantly different optimums from one year to another. While those transient factors are difficult to improve without reliable forecasts, persistent factors influence yield more consistently and therefore represent the best near-term targets for shrinking yield gaps. In this study, multi-year satellite images are used to quantify field-scale maize yield variation in Quzhou County of NCP, and this variation is then analyzed to determine the role of soil type and persistent management factors in explaining yield gaps. Results indicate that (i) remote sensing can provide reasonably reliable estimates of maize yields in this region; (ii) soil type has a clear effect on maize yields, and one that interacts strongly with growing season rainfall amounts; and (iii) on average roughly 20% of yield differences that appear within any one year are related to factors that persist in other years. Overall, the study presents a generalizable methodology of assessing yield gap as well as the proportion arising from persistent factors using satellite data. Our results suggest that the majority of yield gap is dominated by transient factors, and shrinking this gap may require high quality forecasts to make informed optimal management decisions.

A scalable satellite-based crop yield mapper

New advances in satellite data acquisition and processing offer promise for monitoring agricultural lands globally. Using these data to estimate crop yields for individual fields would benefit both crop management and scientific research, especially for areas where reliable ground-based estimates are not currently made. Here we introduce a generalized approach for mapping crop yields with satellite data and test its predictions for yields across more than 17,000 maize fields and 11,000 soybean fields spanning multiple states and years in the Midwestern United States. The method, termed SCYM (a scalable satellite-based crop yield mapper), uses crop model simulations to train statistical models for different combinations of possible image acquisition dates, and these are then applied to Landsat and gridded weather data within the Google Earth Engine platform, where the Landsat is composited to find the “best” dates of observations on a pixel-by-pixel basis. SCYM estimates successfully captured a significant fraction of maize yield variation in all state-years, with a range of 14–58% and an average of 35% for this particular study region and crop. Similar results were observed for soybean, with an average of 32% of yield variation captured. The multi-year yield estimates were also used to examine the temporal persistence of yield advantages for the top yielding fields in different counties, which is one measure of how important factors such as farmer skill are in explaining yield gaps. The strength of the SCYM approach lies in its ability to leverage physiological knowledge embedded in crop models to interpret satellite observations in a scalable way, as it can be readily applied to new crops, regions, and types and timing of remote sensing observations without the need for ground calibration.

Predicting maize kernel number using QTL information

Most maize yield variations are explained by changes in the number of established kernels. Kernel number is, in turn, highly dependent upon ear biomass accumulation around flowering. Both are quantitative traits highly influenced by the environment. Determining the genetic basis of quantitative traits is complex because of usual genetic×environment interactions (GxE). Crop physiology models are proposed to help overcome this problem, as they are structured to predict consequences of GxE interactions based on dynamic responses. We studied the genetic basis of maize kernel number determination at the plant level by conducting two quantitative trait loci (QTL) analysis: (i) on final traits per se (kernel number per plant, KNP, and ear biomass per plant, EB) and (ii) on specific model parameters of well-documented curves describing KNP and EB response to plant growth around flowering. Quantitative trait loci for KNP, EB and model parameters relating KNP and EB to plant growth were determined for 125 RILs of the IBM Syn4 (B73×Mo17) at two environments. We later grew several of these RILs and others from the same population not included in the QTL analysis and attempted to predict EB and KNP based on QTL information coming from each analysis. We hypothesized that doing the QTL analysis on crop physiology model parameters that describe the response of KNP and EB to plant growth is better than using direct QTL information.All traits showed significant variation, and both analyses detected several QTL for the studied traits. Associated QTL for EB and KNP per se did not localize with QTL detected for model parameters. This is the first report describing genomic regions for key physiological traits related to maize biomass partitioning around flowering and kernel set efficiency per unit of accumulated EB 15 days after anthesis. Quantitative trait loci information of model parameters helped to predict accumulated EB and KNP with higher accuracy (r2=0.13 and 0.12, p<0.001, for EB and KNP, respectively) than trying to predict EB and KNP based on QTL detected on final traits per se (r2<0.01 and <0.01, p>0.10, for EB and KNP, respectively). However, predictions using an average crop physiology model parameter across genotypes and individual RIL plant growth gave the highest accuracy (r2=0.46 and 0.37, p<0.001, for EB and KNP, respectively). As such, we identified chromosome areas including potentially relevant genes involved in maize KNP determination, but this information helped to predict KNP at different environments only partially, suggesting other approaches might be needed.

Maize yield patterns on the leeward side of tree windbreaks are site-specific and depend on rainfall conditions in eastern Canada

Tree windbreaks may offer a range of potential advantages in terms of increased crop productivity and stability under climate change while providing multiple external benefits to society. The effects of windbreak on maize yields have not been assessed in a well-documented manner in eastern Canada, which is a major influential barrier limiting their adoption by farmers. In this study, we investigated the spatial distribution of maize grain yield in the leeward side of mature (average age, 30-years-old) single-tree row windbreaks that were located on four farm sites in southern Quebec, Canada. We determined whether the sign and magnitude of windbreak effects on spatial patterns of maize yield varied across contrasting years with respect to rainfall conditions. The greatest yield variation was observed at the tree-crop interface (within 0.5–1H, where H = tree height), where substantial yield reductions occurred. In two sites, the magnitude of negative windbreak effects on maize yield at the tree-crop interface decreases in the wetter years. We found important maize yield variation among sampling positions between 2H and 20H (here considered as the shelter zone), with yield values often significantly higher than at 24H (here considered as a control zone with negligible tree shelter effects). The magnitude of this yield variation in the shelter zone generally decreased in the wetter years. In most cases, we estimated that the net effect of windbreak on maize yield (0.5–20H vs. 24H) was negligible. Significant net positive (16 %) or negative (−6 %) effects of windbreaks on maize production were found at one site only and occurred on two different years. We conclude that the sign and magnitude of windbreak effects on spatial patterns of maize grain vary considerably across farms and depend upon temporal variation in rainfall conditions in eastern Canada.

Divergence of climate impacts on maize yield in Northeast China

Northeast China (NEC), the most productive maize growing area in China, has experienced pronounced climate change. However, the impacts of historical climate changes on maize production and their spatial variations remain uncertain. In this study, we used yield statistics at prefecture scale over the past three decades, along with contemporary climate data, to explore the yield–climate relationship and its spatial variations. At the regional scale, maximum and minimum temperature changes had opposite impacts on maize yield, which increased by 10.0±7.7% in response to a 1°C increase in growing season mean daily minimum temperature (Tmin), but decreased by 13.4±7.1% in response to a 1°C increase in growing season mean daily maximum temperature (Tmax). Variations in precipitation seemed to have small impacts on the maize yield variations (−0.9±5.2%/100mm). However, these responses of maize yield to climate variations were subject to large spatial differences in terms of both the sign and the magnitude. ∼30% of the prefectures showed a positive response of maize yield to rising Tmax, which was in contrast to the negative response at the regional scale. Our results further indicate that the spatial variations in the yield response to climate change can be partly explained by variations in local climate conditions. The growing season mean temperature was significantly correlated with the response of maize yield to Tmax (R=−0.67, P<0.01), which changes from positive to negative when the growing season mean temperature exceeds 17.9±0.2°C. Precipitation became the dominant climatic factor driving maize yield variations when growing season precipitation was lower than ∼400mm, but had a weaker influence than temperature over most of the study area. We conclude that, although NEC is a region spanning only more than one millions of kilometer squares, the divergence of the yield response to climatic variations highlights the need to analyze the yield–climate relationship at fine spatial scales.