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Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80's identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers' fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r2 values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14–77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2–5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large. [ABSTRACT FROM AUTHOR]

The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders--from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders. [ABSTRACT FROM AUTHOR]

Highlights • Integrates advantages of the microwave and optical satellite data. • Optical image is better at representing maize field boundaries than the SAR image. • Both off or in-season optical images are useful to increase the flexibility of our proposed method. • Time-series PolSAR images are able to distinguish maize from other crops. • Random Forest can be used to reduce data redundancy without reducing accuracy. Abstract Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping. [ABSTRACT FROM AUTHOR]

Distance between rows and plants are essential parameters that affect the final grain yield in row crops. This paper presents the results of research intended to develop a novel method to quantify the distance between maize plants at field scale using an Unmanned Aerial Vehicle (UAV). Using this method, we can recognize maize plants as objects and calculate the distance between plants. We initially developed our method by training an algorithm in an indoor facility with plastic corn plants. Then, the method was scaled up and tested in a farmer’s field with maize plant spacing that exhibited natural variation. The results of this study demonstrate that it is possible to precisely quantify the distance between maize plants. We found that accuracy of the measurement of the distance between maize plants depended on the height above ground level at which UAV imagery was taken. This study provides an innovative approach to quantify plant-to-plant variability and, thereby final crop yield estimates. [ABSTRACT FROM AUTHOR]

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [ABSTRACT FROM AUTHOR]

The Crop Environment Resource Synthesis (CERES) models have been developed and utilized for the last 30 years to simulate crop growth in response to climate, soil, genotypes and management across locations throughout the world. We reviewed 215 papers found in the literature that contained field observed data where the CERES models were tested. Over 30 simulated variables of the CERES models have been tested in 43 different countries under various experimental treatments. Across all testing conditions, the CERES models simulated grain yield with a root mean square error (RMSE) of less than 1400 kg/ha (~10% relative error, RE), 1200 kg/ha (~20% RE) and 800 kg/ha (~10% RE) for maize, wheat, and rice, respectively. Phenological development was simulated with less than 7 days difference from the observations in most studies. The CERES models simulated aboveground biomass, harvest index, evapotranspiration, and soil water reasonably well too. The simulations of grain number (up to 4340 root mean square error, RMSE), grain weight (up to 22% error), intercepted photosynthetically active radiation (IPAR, up to 0.41 MJ/plant), leaf area index (LAI, 31.9% error), soil temperature (over 10°C difference), and nitrogen (N) dynamics (up to 80% error) were less accurate. In fact the average error of CERES model simulations tends to be higher under marginal crop growing conditions such as extreme heat or cold, water and nutrient deficit conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The integration of site-specific management principles and conservation tillage practices is a rather unexploited field of research despite their economical and environmental benefits. The objectives of this research were: (1) to investigate the farm economic net return of three conservation tillage practices (NIT – Non-Inversion Tillage, MT – Minimum Tillage and NT – No-Tillage) performed at variable intensity within predefined management zones; the HS – area with a consistently higher yield and LS – area with a consistently low yield, of a maize (Zea mays, L.) field in NE-Italy (2) to identify the most economically sound tillage practice for each management zone using long-term simulation results; (3) to assess the environmental impact of the three tillage systems with regards to soil organic carbon changes, CO2 losses and nitrate leaching using the SALUS model. Field trials were carried out on an 8-ha flat field, situated near Rovigo, NE Italy, on maize (Z. mays, L.). The farm gross margin was higher for NT in the year of study as well as the long-term simulated scenarios that resulted in higher yields over time. The NT tillage practices resulted in higher economic return in the both the HS and LS areas. Total soil carbon was higher in NT due to the crop residues retained on the surface. Nitrate leaching was higher in for the MT and for the LS area. [Copyright &y& Elsevier]

Abstract: The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies. [Copyright &y& Elsevier]

• Maize growth models differ widely in their simulations of soil temperature. • Root-mean-square errors ranged from about 1.5 °C to 5.1 °C. • APSIM, ecosys, JULES, Expert-N, SLFT, and maizsim were the best six models. • Most of the best models used a numeric energy balance on each soil layer. • More recent soil thermal conductivity routines might help improve the models. Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize (Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method "flavors", so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models. [Display omitted] [ABSTRACT FROM AUTHOR]

• Eddy covariance measurements were used to inter-compare 29 maize models in their ability to simulate evapotranspiration (ET). • There was a huge range among the models in their simulations of ET for the initial blind phase, which continued even as more information was supplied. • Medians of the 29 models were generally close to observations. • Both simple and complex models were among the best in their ability to simulate ET. • Widely used models were consistently among the best, presumably because large numbers of users with a wide range of conditions led to improvements. Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. [ABSTRACT FROM AUTHOR]

• Maize growth models differ widely in their simulations of daily evapotranspiration. • Most models fail to sufficiently reduce transpiration after crop maturation. • Most models fail to adequately reproduce effects of low humidity and high windspeed. • The median of models was often but not always the best performing. • Model inter-comparisons suggest avenues to improve simulation of maize ET. Accurate simulation of crop water use (evapotranspiration, ET) can help crop growth models to assess the likely effects of climate change on future crop productivity, as well as being an aid for irrigation scheduling for today's growers. To determine how well maize (Zea mays L.) growth models can simulate ET, an initial inter-comparison study was conducted in 2019 under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Herein, we present results of a second inter-comparison study of 41 maize models that was conducted using more comprehensive datasets from two additional sites - Mead, Nebraska, USA and Bushland, Texas, USA. There were 20 treatment-years with varying irrigation levels over multiple seasons at both sites. ET was measured using eddy covariance at Mead and using large weighing lysimeters at Bushland. A wide range in ET rates was simulated among the models, yet several generally were able to simulate ET rates adequately. The ensemble median values were generally close to the observations, but a few of the models sometimes performed better than the median. Many of the models that did well at simulating ET for the Mead site did poorly for drier, windy days at the Bushland site, suggesting they need to improve how they handle humidity and wind. Additional variability came from the approaches used to simulate soil water evaporation. Fortunately, several models were identified that did well at simulating soil water evaporation, canopy transpiration, biomass accumulation, and grain yield. These models were older and have been widely used, which suggests that a larger number of users have tested these models over a wider range of conditions leading to their improvement. These revelations of the better approaches are leading to model improvements and more accurate simulations of ET. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: Agricultural soils emit about 50% of the global flux of N2O attributable to human influence, mostly in response to nitrogen fertilizer use. Recent evidence that the relationship between N2O fluxes and N-fertilizer additions to cereal maize are non-linear provides an opportunity to estimate regional N2O fluxes based on estimates of N application rates rather than as a simple percentage of N inputs as used by the Intergovernmental Panel on Climate Change (IPCC). We combined a simple empirical model of N2O production with the SOCRATES soil carbon dynamics model to estimate N2O and other sources of Global Warming Potential (GWP) from cereal maize across 19,000 cropland polygons in the North Central Region (NCR) of the US over the period 1964–2005. Results indicate that the loading of greenhouse gases to the atmosphere from cereal maize production in the NCR was 1.7Gt CO2e, with an average 268t CO2e produced per tonne of grain. From 1970 until 2005, GHG emissions per unit product declined on average by 2.8t CO2eha−1 annum−1, coinciding with a stabilisation in N application rate and consistent increases in grain yield from the mid-1970’s. Nitrous oxide production from N fertilizer inputs represented 59% of these emissions, soil C decline (0–30cm) represented 11% of total emissions, with the remaining 30% (517Mt) from the combustion of fuel associated with farm operations. Of the 126Mt of N fertilizer applied to cereal maize from 1964 to 2005, we estimate that 2.2Mt N was emitted as N2O when using a non-linear response model, equivalent to 1.75% of the applied N. [Copyright &y& Elsevier]