Patricio Grassini

Distinguishing between yield advances and yield plateaus in historical crop production trends

Food security and land required for food production largely depend on rate of yield gain of major cereal crops. Previous projections of food security are often more optimistic than what historical yield trends would support. Many econometric projections of future food production assume compound rates of yield gain, which are not consistent with historical yield trends. Here we provide a framework to characterize past yield trends and show that linear trajectories adequately describe past yield trends, which means the relative rate of gain decreases over time. Furthermore, there is evidence of yield plateaus or abrupt decreases in rate of yield gain, including rice in eastern Asia and wheat in northwest Europe, which account for 31% of total global rice, wheat and maize production. Estimating future food production capacity would benefit from an analysis of past crop yield trends based on a robust statistical analysis framework that evaluates historical yield trajectories and plateaus.

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.

High-yield maize with large net energy yield and small global warming intensity

Addressing concerns about future food supply and climate change requires management practices that maximize productivity per unit of arable land while reducing negative environmental impact. On-farm data were evaluated to assess energy balance and greenhouse gas (GHG) emissions of irrigated maize in Nebraska that received large nitrogen (N) fertilizer (183 kg of N⋅ha−1) and irrigation water inputs (272 mm or 2,720 m3 ha−1). Although energy inputs (30 GJ⋅ha−1) were larger than those reported for US maize systems in previous studies, irrigated maize in central Nebraska achieved higher grain and net energy yields (13.2 Mg⋅ha−1 and 159 GJ⋅ha−1, respectively) and lower GHG-emission intensity (231 kg of CO2e⋅Mg−1 of grain). Greater input-use efficiencies, especially for N fertilizer, were responsible for better performance of these irrigated systems, compared with much lower-yielding, mostly rainfed maize systems in previous studies. Large variation in energy inputs and GHG emissions across irrigated fields in the present study resulted from differences in applied irrigation water amount and imbalances between applied N inputs and crop N demand, indicating potential to further improve environmental performance through better management of these inputs. Observed variation in N-use efficiency, at any level of applied N inputs, suggests that an N-balance approach may be more appropriate for estimating soil N2O emissions than the Intergovernmental Panel on Climate Change approach based on a fixed proportion of applied N. Negative correlation between GHG-emission intensity and net energy yield supports the proposition that achieving high yields, large positive energy balance, and low GHG emissions in intensive cropping systems are not conflicting goals.

Impact of derived global weather data on simulated crop yields.

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present studys objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

Large-scale on-farm implementation of soil moisture-based irrigation management strategies for increasing maize water productivity

Irrigated maize is produced on about 3.5 Mha in the U.S. Great Plains and western Corn Belt. Most irrigation water comes from groundwater. Persistent drought and increased competition for water resources threaten long-term viability of groundwater resources, which motivated our research to develop strategies to increase water productivity without noticeable reduction in maize yield. Results from previous research at the University of Nebraska-Lincoln (UNL) experiment stations in 2005 and 2006 found that it was possible to substantially reduce irrigation amounts and increase irrigation water use efficiency (IWUE) and crop water use efficiency (CWUE) (or crop water productivity) with little or no reduction in yield using an irrigation regime that applies less water during growth stages that are less sensitive to water stress. Our hypothesis was that a soil moisture-based irrigation management approach in research fields would give similar results in large production-scale, center-pivot irrigated fields in Nebraska. To test this hypothesis, IWUE, CWUE, and grain yields were compared in extensive on-farm research located at eight locations over two years (16 site-years), representing more than 600 ha of irrigated maize area. In each site-year, two contiguous center-pivot irrigated maize fields with similar topography, soil properties, and crop management practices received different irrigation regimes: one was managed by UNL researchers, and the other was managed by the farmer at each site. Irrigation management in farmer-managed fields relied on the farmers’ traditional visual observations and personal expertise, whereas irrigation timing in the UNL-managed fields was based on pre-determined soil water depletion thresholds measured using soil moisture sensors, as well as crop phenology predicted by a crop simulation model using a combination of real-time (in-season) and historical weather data. The soil moisture-based irrigation regime resulted in greater soil water depletion, which decreased irrigation requirements and enabled more timely irrigation management in the UNL-managed fields in both years (34% and 32% less irrigation application compared with farmer-managed fields in 2007 and 2008, respectively). The average actual crop evapotranspiration (ETC) for the UNL- and farmer-managed fields for all sites in 2007 was 487 and 504 mm, respectively. In 2008, the average UNL and average farmer-managed field had seasonal ETC of 511 and 548 mm, respectively. Thus, when the average of all sites is considered, the UNL-managed fields had 3% and 7% less ETC than the farmer-managed fields in 2007 and 2008, respectively, although the percentage was much higher for some of the farmer-managed fields. In both years, differences in grain yield between the UNL and farmer-managed fields were not statistically significant (p = 0.75). On-farm implementation of irrigation management strategies resulted in a 38% and 30% increase in IWUE in the UNL-managed fields in 2007 and 2008, respectively. On average, the CWUE value for the UNL-managed fields was 4% higher than those in the farmer-managed fields in both years. Reduction in irrigation water withdrawal in UNL-managed fields resulted in

High-yield maize–soybean cropping systems in the US Corn Belt

32.00 to

Prospects for Increasing Sugarcane and Bioethanol Production on Existing Crop Area in Brazil

74.10 ha-1 in 2007 and

Yield gap analysis of rainfed wheat demonstrates local to global relevance

44.46 to

Can Bangladesh produce enough cereals to meet future demand

66.50 ha-1 in 2008 in energy saving and additional net return to the farm income. The results from this study can have significant positive implications in future irrigation management of irrigated maize systems in regions with similar soil and crop management practices.

Estimating yield gaps at the cropping system level

The USA accounts for 38 and 35% of global maize and soybean production, producing a respective 320 and 84 Mt of these crops annually. More than 85% of those totals are produced in the north-central region known as the ‘Corn Belt’, where continuous maize and 2-year maize–soybean rotation are the dominant cropping systems. This chapter describes the climate, soil, and management practices of high-yield maize–soybean cropping systems in the Corn Belt. Major drivers for higher yields and resource-use efficiency are evaluated and opportunities for further improvement are discussed. Yield and input-use efficiency of maize and soybean in the US Corn Belt have increased steadily during the last 40 years as a result of (1) continuous genetic improvement, (2) intermittently phased periods of agronomic improvement, and (3) the synergistic interaction of improved genetics and agronomy. Future increases may be difficult to achieve as on-farm yields approach yield potential; however, some of the yield gap between on-farm yield and simulated yield potential can still be captured by fine-tuning crop management in a manner that increases yield, while simultaneously reducing the resource input amount or cost. Indeed, substantive opportunities exist for increased input-use efficiency by scheduling just-in-time irrigation events of the minimum amount needed, and by optimizing management of N fertilizer to be temporally and spatially effective.