Rainfed Maize Yield Research Articles

Efficacy of planting date adjustment as a cultivation strategy to cope with drought stress and increase rainfed maize yield and water-use efficiency

Given the frequent drought pressure caused by the unpredictable and limited precipitation concurrent with global climate change, highly efficient cultivation technologies have been increasingly recognized by various levels of scientific communities. Understanding plant–environment relationships in rainfed dry land may help maximize crop productivity while improving water utilization of farmland. Field experiments were conducted in 2012–2014 at the Dryland Maize Experimental Station of the Northwest A&F University, China to determine the effects of possible drought stress and the environmental factors involved during sowing at different sowing dates on maize vegetative growth and grain yield (Zea mays L.), as well as water-use efficiency (WUE=grain yield per unit of seasonal evapotranspiration). Six planting date (PD) treatments with sowing performed for 6 days from April 10 to May 10 were designed. Results showed that the maize growth period was shortened with the postponement of planting time. The vegetative growth stage and the overlapping stages of vegetative and reproductive growth varied by 4–19 days among various PD practices. However, the reproductive growth stage duration was relatively stable and varied by 3–5 days only among the different PD practices. Within a certain time range, dry matter production per plant did not obviously change across the different PD treatments. However, the dry matter accumulation in the ear after flowering, the yield, and the WUE in the treatments under appropriate PDs (April 16–April 28) were 2.2%–28.8%, 2.3%–24.7%, and 6.6%–15.2% higher, respectively, than those in the early or delayed PDs. These findings resulted from the changes in soil water content with PD adjustment. Yield was highly correlated to the soil moisture content during PD, the rainfall before silking, the effective accumulated temperature after silking, and the sunshine hours after silking. Moreover, the thousand-grain weight was highly correlated with the sunshine hours after silking. In the early PDs, the main factor that affected maize yield was the low content of soil moisture, which generated low effective ear number per unit area and seedling emergence ratio. In the late PDs, the main factors that influenced maize yield were the low effective accumulated temperature and the short sunshine hours during the reproductive growth stage, which produced less dry matter accumulation after silking and lower thousand-grain weight. Under suitable sowing time, the actual harvest ear number per hectare and dry matter accumulation of female ear after silking increased. Similarly, the maize yield and WUE increased. By considering the ecological factors and study results, we recommend that the most suitable sowing time for maize should be determined on the basis of the soil moisture content before April 28. As such, we can effectively achieve high yield and avoid drought in the study region. Overall, the results can provide effective cultivation techniques to prevent drought stress in spring maize in the present agro-ecosystem of northern China and other similar areas.

Climate change impacts and potential benefits of heat-tolerant maize in South Asia

Maize is grown by millions of smallholder farmers in South Asia (SA) under diverse environments. The crop is grown in different seasons in a year with varying exposure to weather extremes, including high temperatures at critical growth stages which are expected to increase with climate change. This study assesses the impact of current and future heat stress on maize and the benefit of heat-tolerant varieties in SA. Annual mean maximum temperatures may increase by 1.4–1.8 °C in 2030 and 2.1–2.6 °C in 2050, with large monthly, seasonal, and spatial variations across SA. The extent of heat stressed areas in SA could increase by up to 12 % in 2030 and 21 % in 2050 relative to the baseline. The impact of heat stress and the benefit from heat-tolerant varieties vary with the level of temperature increase and planting season. At a regional scale, climate change would reduce rainfed maize yield by an average of 3.3–6.4 % in 2030 and 5.2–12.2 % in 2050 and irrigated yield by 3–8 % in 2030 and 5–14 % in 2050 if current varieties were grown under the future climate. Under projected climate, heat-tolerant varieties could minimize yield loss (relative to current maize varieties) by up to 36 and 93 % in 2030 and 33 and 86 % in 2050 under rainfed and irrigated conditions, respectively. Heat-tolerant maize varieties, therefore, have the potential to shield maize farmers from severe yield loss due to heat stress and help them adapt to climate change impacts.

Assessment of Impacts of Climate Change and Adaptation Measures for Maize Production in East Sikkim, India

An investigation was carried out to assess the impacts of climate change on rainfed maize yield using a yield response to water stress model (AquaCrop) and to identify suitable adaptation options to minimize the negative impacts on maize yield in East Sikkim, North East India. Crop management and yield data was collected from the field experimental plots for calibration and validation of the model for the study area. The future climate data was developed for two IPCC emission scenarios A2 and B2 based on the global climate model HadCM3 with downscaling of climate to finer spatial resolution using the statistical downscaling model, SDSM. The impact study revealed that there is an expected reduction in maize yield of 12.8, 28.3 and 33.9% for the A2 scenario and 7.5, 19.9 and 29.9% for the B2 scenario during 2012-40, 2041-70 and 2071-99 respectively compared to the average yield simulated during the period of 1961-1990 with observed climate data. The maize yield of same variety under future climate can be maintained or improved from current level by changing planting dates, providing supplement irrigation and managing optimum nutrient.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.15-27

Environment and Nitrogen Influence on Rainfed Maize Yield and Quality

Kernel quality is determined by environmental conditions and fertilization practices. However, the effect of those factors needs to be studied for maize (Zea mays L.) grown in rainfed conditions in permanent‐raised beds as planting system. Long‐term plots in rotation with wheat (Triticum aestivum L.) were established in 2002 with this planting technology. The objective was to assess the effect of crop seasons and three N rates (0, 60, and 120 kg N ha–1) on yield and kernel quality. Results indicated that yield and kernel quality were mostly affected by the effect of crop seasons, except yield and kernel protein (KP) that improved 7.3 and 10.4% respectively, as N application increased from 0 to 60 kg ha–1. Effect of crop season was estimated by means of rainfall and growing degree days (GDD). Seasonal rainfall accounted for 66% of yield variation. Amount of rainfall from tasseling to physiological maturity was associated to 1000‐kernel weight (KW) and floating index (FI). Similarly, as GDD increased from 1310 to 1475, FI improved from 84 to 40% Even though the effect of crop seasons was significant on test weight (TW), no relationship was identified with rainfall and GDD likely due to an outlier data point. In average, environment contributed more heavily to measured parameters than N management. Normalized difference vegetative index (NDVI) readings collected at early growth stages showed to be a potential tool to predict final TW.

Effects of climatic factors, drought risk and irrigation requirement on maize yield in the Northeast Farming Region of China

SUMMARYDrought risk is considered to be among the main limiting factors for maize (Zea maysL.) production in the Northeast Farming Region of China (NFR). Maize yield data from 44 stations over the period 1961–2010 were combined with data from weather stations to evaluate the effects of climatic factors, drought risk and irrigation requirement on rain-fed maize yield in specific maize growth phases. The maize growing season was divided into four growth phases comprising seeding, vegetative, flowering and maturity based on observations of phenological data from 1981 to 2010. The dual crop coefficient was used to calculate crop evapotranspiration and soil water balance during the maize growing season. The effects of mean temperature, solar radiation, effective rainfall, water deficit, drought stress days, actual crop evapotranspiration and irrigation requirement in different growth phases were included in the statistical model to predict maize yield. During the period 1961–2010, mean temperature increased significantly in all growth phases in NFR, while solar radiation decreased significantly in southern NFR in the seeding, vegetative and flowering phases. Effective rainfall increased in the seeding and vegetative phases, reducing water deficit over the period, whereas decreasing effective rainfall over time in the flowering and maturity phases enhanced water deficit. An increase in days with drought stress was concentrated in western NFR, with larger volumes of irrigation needed to compensate for increased dryness. The present results indicate that higher mean temperature in the seeding and maturity phases was beneficial for maize yield, whereas excessive rainfall would damage maize yield, in particular in the seeding and flowering phases. Drought stress in any growth stage was found to reduce maize yield and water deficit was slightly better than other indicators of drought stress for explaining yield variability. The effect of drought stress was particularly strong in the seeding and flowering phases, indicating that these periods should be given priority for irrigation. The yield-reducing effects of both drought and intense rainfall illustrate the importance of further development of irrigation and drainage systems for ensuring the stability of maize production in NFR.

Simulation of growth and yield of rainfed maize under varied agronomic management and changing climatic scenario in Nawalparasi, Nepal

Correction: Figure 3 was corrupted and so the PDF was replaced on 29th December 2016 with the corrected Figure 3.A field experiment and simulation modeling study in combination for different maize cultivars planted at different sowing dates were accomplished at Kawasoti-5, Nawalparasi during spring season of 2013 to assess the impact of climate change scenario as predicted by IPCC in rainfed spring maize by using CSM-CERES-Maize model. Result showed that RML-4/RML-17 produced higher kernel rows/ ear (13.77), kernel per row (30.42) and test weight (244.9 g). Significantly higher grain yield was also found for RML-4/RML-17 (6.03 t/ha) compared to Poshilo makai-1 (4.73 t/ha), Arun-2 (3.55 t/ha) and Local (2.92 t/ha). Earlier sowing date (7th April) actually produced higher kernel/row (27.97), kernel rows/ear (12.89) and 1000 grain weight (230 g). Significantly higher grain yield (5.13t/ha) was obtained in earlier sowing date (7th April). The CSM-CERES-Maize model was calibrated and found well validated with days to anthesis (RMSE= 0.426 day and D-index= 0.998), days to physiological maturity (RMSE=0.674 day and D-index= 0.999), number of grain/m2 at maturity (RMSE= 85.287 grain /m2 and D-index= 0.993), unit weight at maturity (RMSE=0.012 g/kernel and D-index= 0.854) and grain yield (RMSE=54.94 kg/ha and D-index= 1.00). The model was found sensitive to climate change parameters. The sensitivity for various climate change parameter indicated that there was severely decreased trend in simulated rainfed spring maize yield with the increment of maximum and minimum temperature, decrease in solar radiation and decrease carbondioxide concentration. Even 2°C rise in temperature can decrease around 15-20% yield of spring maize and this negative effect was even more pronounced in hybrid than other cultivars.Journal of Maize Research and Development (2015) 1(1):123-133DOI: http://dx.doi.org/10.5281/zenodo.34289

Forecasting climate change impacts and evaluation of adaptation options for maize cropping in the hilly terrain of Himalayas: Sikkim, India

An investigation was carried out to assess the climate change impacts on rainfed maize yield using AquaCrop and CERES–maize crop simulation models, and evaluation of adaptation measures were performed for Sikkim state of India. Data related to crop phenology retrieved from the field experiments were used to calibrate and validate the crop models for three representative sites. Climate projections of six global circulation models (ECHAM5, CCSM, HadCM3, CSIRO-MK3.0, CGCM3.1, and MIROC3.2) for scenarios A2 and B2 were bias-corrected at station scale by power law transformation. Simulation results by the two crop models indicate a significant declination in the yield of NLD-White variety of maize ranging from 4.7 ± 1.4 to 20.4 ± 7.2 % for the future time windows under A2 scenario and 2.5 ± 0.9 to 15.8 ± 5.7 % under B2 scenario relative to the yield simulated for the baseline period of 1991–2000. It is also observed that, for a particular temperature, yield remarkably increases with escalated carbon dioxide (CO2) concentration. On contrary, increase in temperature reduces the yield at a particular CO2 concentration. The overall decline in the yield under future climate scenarios can be alleviated by early planting, appropriate nutrient management, introducing supplementary irrigation, and shifting to heat-tolerant varieties.

Application of GIS and Geographically Weighted Regression to Evaluate the Spatial Non-Stationarity Relationships Between Precipitation vs. Irrigated and Rainfed Maize and Soybean Yields

Understanding the relationship between the spatial distribution of precipitation and crop yields on large scales (i.e., county, state, regional) while accounting for the spatial non-stationarity can help managers to better evaluate the long-term trends in agricultural productivity to make better assessments in food security, policy decisions, resource assessments, land and water resources enhancement, and management decisions. A relatively new technique, geographically weighted regression (GWR), has the ability to account for spatial non-stationarity with space. While its application is growing in other scientific disciplines (i.e., social sciences), the application of this new technique in agricultural settings has not been practiced. The geographic information system (GIS), along with two different statistical techniques [GWR and conventional ordinary least square regression (OLS)], was utilized to analyze the relationships between various precipitation categories and irrigated and rainfed maize and soybean yields for all 93 counties in Nebraska from 1996 to 2008. Precipitation was spatially interpolated in ArcGIS using a spline interpolation technique with zonal statistics. Both measured and GWR- and OLS-predicted yields were correlated to spatially interpolated annual (January 1 to December 31), seasonal (May 1 to September 30), and monthly (May, June, July, August, and September) precipitation for each county. Statewide average annual precipitation in Nebraska from 1996 to 2008 was 564 mm, with a maximum of 762 mm and minimum of 300 mm. Mean precipitation decreased gradually from May to September during the growing season. County average yields followed the same temporal trends as precipitation. When the OLS regression model was used, there was a general trend of linear correlation between observed yield and long-term average mean annual total precipitation with a varying coefficient of determination (R2). For rainfed crops, 67% of the variability in mean yield was explained by the mean annual precipitation. About 23% and 17% of the variability in mean yield was explained by mean annual precipitation for irrigated maize and soybean, respectively. However, the performance of the GWR technique in predicting the yields from spatially interpolated precipitation for irrigated and rainfed maize and soybean was significantly better than the performance of the OLS model. For both rainfed maize and soybean, 77% to 80% of the variation in yield was explained by the mean annual precipitation alone. For irrigated crops, 42% of the variation in the yield was explained by the mean annual precipitation. For rainfed crops, there was a strong correlation between seasonal precipitation and yield, with R2 values of 0.73 and 0.76 for maize and soybean, respectively. The mean annual total precipitation was a better predictor of rainfed maize yield than rainfed soybean yield. On a statewide average, July precipitation as a predictor had the greatest correlation with yields of both maize and soybean. June, July, and August precipitation had greater impact on maize yield than on soybean under rainfed conditions due to more sensitivity of maize to water stress than soybean. For irrigated yields, July precipitation had more impact on soybean yield than on maize. The performance of the GWR technique was superior to the OLS model in analyzing the relationship between yield and precipitation. The superiority of the GWR technique to OLS is mainly due to its ability to account for the impact of spatial non-stationarity on the precipitation vs. yield relationships.

Modelling weekly rainfall data for crop planning in a sub-humid climate of India

In this study, two and three-parameter probability distributions have been compared to identify the most appropriate distribution to describe the weekly rainfall data in a sub-humid climate of India. The “best” distribution among different data sets has been identified using probability plot and Anderson–Darling (AD) test for goodness-of-fit, along with the appropriateness of estimated percentiles. One single probability distribution has not been found appropriate to represent all the data sets though Weibull distribution has been found promising for most of the data sets. Gamma probability distribution, which is generally employed for describing weekly rainfall data, was found to grossly approximate the underlying process. Likelihood ratio (LR) test revealed that three-parameter distributions did not significantly improve the fit over two-parameter distributions within the same family. The developed models for different data sets have been employed for computing minimum assured amount of rainfall at different probability levels. Minimum assured rainfall at 40% probability level was found to be in close agreement with the long-term average weekly rainfall data as depicted by L2 and Chebycheff norms. The effect of departure from minimum assured rainfall at 70% probability level on yield of rainfed maize in the region has also been studied using the available experimental data collected from three different field experiments. The effect of intervening drought was found to be most serious in reducing the crop yield by 37–58% as compared to other scenarios representing rainfall amount and its distribution under a sub-humid climate.

Climatic factors affecting annual maize yields in the valley of Oaxaca, Mexico

Locally measured climatic variables, such as temperature and precipitation, clearly affect agriculture, particularly in semi-arid regions. Some climate/agriculture applications, however, do better to focus on meso- and large-scale atmospheric and oceanic circulatory influences on the location of interest. In the Valley of Oaxaca in the highlands of southern Mexico for example, rain-fed maize yields are as sensitive to interannual variations in the Southern Oscillation Index (SOI) and a synoptic index of summer 850 hPa-level temperatures in the Mexico–USA border region as they are to locally measured precipitation, a variable to which yields are also strongly related. From 1978 to 1990, August precipitation explains 62 per cent of the variation in yields on the area planted in rain-fed maize in the Valley of Oaxaca, a region highly dependent on that crop. An index derived from 850 hPa-level temperatures and the annual average value of the SOI are comparable, explaining 48 per cent and 68 per cent of maize-yield variation respectively. This suggests that useful information for agricultural decision-making in the Valley of Oaxaca can be obtained by monitoring ENSO and the development of large-scale circulatory patterns. Unlike local observations, these larger scale phenomena may provide advance warning of precipitation conditions during the agricultural season upon which so many people's livelihoods in the region depend. © 1997 Royal Meteorological Society.

Evaluation of Two Maize Models for Nine U.S. Locations

AbstractCrop models can be evaluated based on accuracy in simulating several years' yields for one location or on accuracy in simulating long‐term mean yields for several locations. Our objective was to see how the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and a new version of CERES‐Maize (Crop‐Environment Resource Synthesis) simulate grain yield of rainfed maize (Zea mays L.). We tested the models at one county in each of nine states: Minnesota, New York, Iowa, Illinois, Nebraska, Missouri, Kansas, Louisiana, and Texas (MN, NY, IA, IL, NE, MO, KS, LA, and TX). Simulated grain yields were compared with grain yields reported by the National Agricultural Statistical Service (NASS) for 1983 to 1992. In each county we chose a soil commonly used in maize production, and we used measured weather data. Mean simulated grain yield for each county was always within 5% of the mean measured grain yield for the location. Within locations, measured grain yield was regressed on simulated grain yields and tested to see if the slope was significantly different from 1.0 and if the y‐intercept was significantly different from 0.0, both at the 95% confidence level. Only at MN, NY, and NE for ALMANAC and at MN, NY, and TX for CERES was slope significantly different from 1.0 or intercept significantly different from 0.0. The CVs of simulated grain yields were similar to the those of measured yields at most sites. Also, both models were appropriate for predicting an individual year's yield for most counties. Values for plant parameters, such as heat units for development and the harvest index, and values for soil parameters describing soil water‐holding capacity offer users reasonable inputs for simulating maize grain yield over a wide range of locations.

Tillage and mulch effects on soil physical environment, root growth, nutrient uptake and yield of maize and wheat on an Alfisol in north-west India

Field experiments were conducted for 6 years on a silty clay loam to study the effect of soil management on soil physical properties, root growth, nutrient uptake and yield of rainfed maize ( Zea mays L.) and wheat ( Triticum aestivum L.) grown in a sequence. Treatments were: no-tillage (NT), NT+pine needle mulch at a rate of 10 t ha −1 (NT+M), conventional tillage (CT), CT+pine needle mulch at a rate of 10 t ha −1 (CT+M) and deep tillage (DT). The soil is classified as a Typic Hapludalf and has compact sub-surface layers. The NT treatment increased the bulk density of the surface layer but this problem was not observed in the no-tilled treatment having mulch at the surface (NT+M). The CT+M and NT+M treatments favourably moderated the hydro-theregime resulting in greater root growth, nutrient uptake and grain yields of maize and wheat. The DT treatment, imposed only once, at the beginning of the study, also enhanced root growth and grain yields. The yields were similar to the mulched treatments for maize and somewhat less than the mulched treatments for wheat. Mulched treatments generally showed significantly greater total uptake of N, P and K than corresponding unmulched ones. Since NT+M was comparable to CT for maize and superior for wheat, the latter is preferable since it does not require ellaborate tillage.