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Copyright of Avances is the property of Instituto de Informacion Cientifica y Tecnologica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In areas with a limited or non-existent network of observing stations, it is critical to assess the applicability of gridded datasets. This study examined the agreement of Agri4Cast and E-OBS at two spatial resolutions (10 km (EOBS-0.1) and 25 km (EOBS-0.25)) in 13 Mediterranean stations nearby to wheat crops and how this agreement may influence simulated potential development and production with the crop simulation model (CSM) CERES-Wheat in historical and near-future (2021–2040) (NF) periods. A wide range of sensitivity tests for maximum and minimum air temperatures and impact response surfaces were used for the future projections. EOBS-0.1 showed the lowest discrepancies over observations. It underestimated statistical measures of temperature and precipitation raw data and their corresponding extreme indices and overestimated solar radiation. These discrepancies caused small delays (5–6 days, on average) in crop development and overestimations (8%) in grain production in the reference period. In the NF, the use of EOBS-0.1 reduced by a few (2–3) days the biases in crop development, while yield responses differed among stations. This research demonstrated the ability of EOBS-0.1 for agricultural applications that depend on potential wheat development and productivity in historical and future climate conditions expected in the Mediterranean basin. [ABSTRACT FROM AUTHOR]

Water availability for irrigation farming is one of the greatest challenges associated with the increasing spatio-temporal effects of climate change and variability on tomato production, especially in tropical regions. This study was conducted to demonstrate the combined effect of irrigation and nutrient management as a water-saving strategy to maximize nutrient and water productivity in tomato production. The research was conducted in a screen house at the CSIR-Crops Research Institute (CSIR-CRI), Kumasi, Ghana using the split-plot design in the 2020 and 2021 cropping seasons. The main plot consisted of a single full irrigation at 100% recommended crop water requirement and two deficit irrigation levels (75 and 50% of the crop water requirement). The subplots consisted of two nutrient amendments (inorganic fertilizer and organic compost) and a control (without any nutrient amendment). Data from the research was used to parameterize the DSSAT CCROPGRO model to simulate the interactive effect of irrigation and nutrient management on the yield of tomatoes. Plant height and stem girth did not have a specific influence on tomato yield, but the number of branches had a positive effect on tomato yield. The combined use of inorganic fertilizer and full irrigation was found to improve tomato yield up to 7691.4 and 9009.9 kg/ha whereas treatment with no fertilizer application at 50% deficit irrigation recorded the lowest tomato yield of 1423.9 and 1739.2 kg/ha in 2020 and 2021, respectively. For the two deficit irrigations (50 and 75% ETc), organic compost produced the highest tomato yield. Deficit irrigation recorded higher crop water productivity (CWP) compared to full irrigation. At 50% deficit irrigation, organic compost recorded the highest CWP of 4.54 kg/m3 in 2020 while inorganic fertilizer recorded the highest CWP of 5.52 kg/m3 in 2021. No fertilizer at full irrigation recorded the lowest CWP of 1.37 and 1.67 kg/m3 in 2020 and 2021, respectively. This study has revealed that deficit irrigation with organic compost has the same effect on yield and water productivity as full irrigation with inorganic fertilizer. The strong agreement observed between the measured and simulated yields under the different irrigation and nutrient management shows that the DSSAT CROPGRO tomato model can be used to simulate tomato fruit yield under future climate scenarios. However, the general overestimation of the measured tomato yield shows the limitations of the model to simulate the real-world complexity of cropping systems under controlled conditions. This calls for more research into crop system modeling in controlled environment agriculture.

Abstract Background Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies.

Background: Predicting the phenotype from the genotype is one of the major contemporary challenges in biology. This challenge is greater in plants because their development occurs mostly post-embryonically under diurnal and seasonal environmental fluctuations. Most current crop simulation models are physiology-based models capable of capturing environmental fluctuations but cannot adequately capture genotypic effects because they were not constructed within a genetics framework. Results: We describe the construction of a mixed-effects dynamic model to predict time-to-flowering in the common bean (Phaseolus vulgaris L.). This prediction model applies the developmental approach used by traditional crop simulation models, uses direct observational data, and captures the Genotype, Environment, and Genotype-by-Environment effects to predict progress towards time-to-flowering in real time. Comparisons to a traditional crop simulation model and to a previously developed static model shows the advantages of the new dynamic model. Conclusions: The dynamic model can be applied to other species and to different plant processes. These types of models can, in modular form, gradually replace plant processes in existing crop models as has been implemented in BeanGro, a crop simulation model within the DSSAT Cropping Systems Model. Gene-based dynamic models can accelerate precision breeding of diverse crop species, particularly with the prospects of climate change. Finally, a gene-based simulation model can assist policy decision makers in matters pertaining to prediction of food supplies. [ABSTRACT FROM AUTHOR]

In areas with a limited or non-existent network of observing stations, it is critical to assess the applicability of gridded datasets. This study examined the agreement of Agri4Cast and E-OBS at two spatial resolutions (10 km (EOBS-0.1) and 25 km (EOBS-0.25)) in 13 Mediterranean stations nearby to wheat crops and how this agreement may influence simulated potential development and production with the crop simulation model (CSM) CERES-Wheat in historical and near-future (2021–2040) (NF) periods. A wide range of sensitivity tests for maximum and minimum air temperatures and impact response surfaces were used for the future projections. EOBS-0.1 showed the lowest discrepancies over observations. It underestimated statistical measures of temperature and precipitation raw data and their corresponding extreme indices and overestimated solar radiation. These discrepancies caused small delays (5–6 days, on average) in crop development and overestimations (8%) in grain production in the reference period. In the NF, the use of EOBS-0.1 reduced by a few (2–3) days the biases in crop development, while yield responses differed among stations. This research demonstrated the ability of EOBS-0.1 for agricultural applications that depend on potential wheat development and productivity in historical and future climate conditions expected in the Mediterranean basin.

Tea (Camellia sinensis L.) is one of the most highly consumed beverages globally after water. Several countries import large quantities of tea from other countries to meet domestic needs. Therefore, accurate and timely prediction of tea yield is critical. The previous studies used statistical, deep learning, and machine learning techniques for tea yield prediction, but crop simulation models have not yet been used. However, the calibration of a simulation model for tea yield prediction and the comparison of these approaches is needed regarding the different data types. This research study aims to provide a comparative study of the methods for tea yield prediction using the Food and Agriculture Organization (FAO) of the United Nations AquaCrop simulation model and machine learning techniques. We employed weather, soil, crop, and agro-management data from 2016 to 2019 acquired from tea fields of the National Tea and High-Value Crop Research Institute (NTHRI), Pakistan, to calibrate the AquaCrop simulation model and to train regression algorithms. We achieved a mean absolute error (MAE) of 0.45 t/ha, a mean squared error (MSE) of 0.23 t/ha, and a root mean square error (RMSE) of 0.48 t/ha in the calibration of the AquaCrop model and, out of the ten regression models, we achieved the lowest MAE of 0.093 t/ha, MSE of 0.015 t/ha, and RMSE of 0.120 t/ha using 10-fold cross-validation and MAE of 0.123 t/ha, MSE of 0.024 t/ha, and RMSE of 0.154 t/ha using the XGBoost regressor with train test split. We concluded that the machine learning regression algorithm performed better in yield prediction using fewer data than the simulation model. This study provides a technique to improve tea yield prediction by combining different data sources using a crop simulation model and machine learning algorithms. [ABSTRACT FROM AUTHOR]

Abstract A broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.

The paper traces the evolution of agricultural remote sensing in India through its four phases: initial exploratory and aerial data based (1969–1982), IRS Utilization Program led (1983–1995), post-IRS-1C launch (1996–2011) and since the establishment of a dedicated institution for EO-based crop forecasting and other agricultural applications (2012 onward). Published field-scale studies are discussed before introducing the submissions to the special issue of the journal, namely Advanced Geospatial Technologies for Agriculture with Special emphasis on field level Applications. A total of 17 articles grouped under crop discrimination and mapping, crop yield estimation, crop suitability analysis, irrigation management and others (UAV, fodder and field boundary extraction) and 15 of these describe work over India and remaining two have Iran as their study area. These studies are linked to other published work as well as challenges under major issues of data access, use of UAV, use of smartphones, data processing and crop simulation models. Taken together, these articles report substantial improvements in capabilities of field-scale monitoring for developing new class of agricultural applications in India as well as other regions in the world with small farm holdings. [ABSTRACT FROM AUTHOR]

Crop growth and yield are influenced by the genetic potential of the cultivar, soil, weather, and cultivation practices, i.e., sowing date, irrigation and fertilizer amount, and biotic stresses. Temporal variation in yield and growth has been largely forecasted using climate as a predictor, which can be achieved by using either an empirical or crop simulation approach for a given location. Climate and soil data collected over agricultural land regularly aid in crop growth monitoring, as well as crop vitality assessment. Crop simulation models (CSM) that have been successful in field-scale applications are now being implemented in GIS framework to simulate and monitor crop growth with remote sensing inputs, allowing for sensitive evaluations of seasonal weather conditions, local variability, and crop management signals. This research was designed to monitor the growth of three varieties of kiwifruit, i.e., Hayward grafted, Green-flesh, and Hayward, in four different localities: Hazaro (Attock), Simli Dam (Pind Begwal), GPU (germ plasm unit) Arid Agriculture University Rawalpindi and ZTBL (Zarai Taraqiati Bank Limited) Farm Islamabad, each of which has different soil and weather conditions. Soil proximal sensors were used to measure soil characteristics, and data loggers were installed in each field to monitor the weather parameters to collect data that influences crops. In this study, we used a quantitative method and GIS-integrated data to assess the impact of soil and climate on kiwifruit growth. It can help policy makers and researchers to identify new agro-climatic zones in Pakistan’s semi-arid regions for kiwifruit farming based on this data. In this study, we found that kiwi is very susceptible to temperatures above 40 °C, which cause mortality in kiwifruits plants. Morphological data with respect to soil and climate results showed that green-fleshed and Hayward varieties performed slightly better than Hayward grafted, which was most susceptible to diseases and heat damage.

Tea (Camellia sinensis L.) is one of the most highly consumed beverages globally after water. Several countries import large quantities of tea from other countries to meet domestic needs. Therefore, accurate and timely prediction of tea yield is critical. The previous studies used statistical, deep learning, and machine learning techniques for tea yield prediction, but crop simulation models have not yet been used. However, the calibration of a simulation model for tea yield prediction and the comparison of these approaches is needed regarding the different data types. This research study aims to provide a comparative study of the methods for tea yield prediction using the Food and Agriculture Organization (FAO) of the United Nations AquaCrop simulation model and machine learning techniques. We employed weather, soil, crop, and agro-management data from 2016 to 2019 acquired from tea fields of the National Tea and High-Value Crop Research Institute (NTHRI), Pakistan, to calibrate the AquaCrop simulation model and to train regression algorithms. We achieved a mean absolute error (MAE) of 0.45 t/ha, a mean squared error (MSE) of 0.23 t/ha, and a root mean square error (RMSE) of 0.48 t/ha in the calibration of the AquaCrop model and, out of the ten regression models, we achieved the lowest MAE of 0.093 t/ha, MSE of 0.015 t/ha, and RMSE of 0.120 t/ha using 10-fold cross-validation and MAE of 0.123 t/ha, MSE of 0.024 t/ha, and RMSE of 0.154 t/ha using the XGBoost regressor with train test split. We concluded that the machine learning regression algorithm performed better in yield prediction using fewer data than the simulation model. This study provides a technique to improve tea yield prediction by combining different data sources using a crop simulation model and machine learning algorithms.

Crop simulation models based on physiological processes are valuable tools for simulating crop production, estimating agronomic efficiency, and developing optimal management practices for sustainable agriculture. The objectives of this study were to calibrate the DSSAT model for the P-7928 corn cultivar and explore management strategies to improve crop production, which included sowing density, fertilization levels and edaphoclimatic conditions. The DSSAT model showed excellent performance. The simulated yields were related to those observed, and showed a Willmott index of 0.96. The square root of the mean square of the normalized error behaved with an acceptable value of 28 % and R2 of 0.985, which shows the good fit of model and shows that it can be used to simulate the crop yield and its physiological components under Cuba conditions. The simulations performed show higher yield, when combining the sowing of 7 plants m-2 and a nitrogen fertilization of 150 kg ha-1. The comparative study of the yields in different edaphoclimatic conditions shows the influence of the latter on the crop response. [ABSTRACT FROM AUTHOR]

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 succession of main-season soybean (Glycine max (L.) Merr.) with off-season maize (Zea mays L.) is an important Brazilian agricultural system contributing to increased grain production without the need for crop land expansion. Yield-gap studies that identify the main factors threatening these crops are pivotal to increasing food security in Brazil and globally. Therefore, the aim of the present study was to determine, for the soybean–off-season-maize succession, the magnitude of the grain and revenue yield gap (YG) caused by water deficit (YGW) and suboptimal crop management (YGM), and to propose strategies for closing these gaps in different Brazilian regions. The ensemble of three previously calibrated and validated models (FAO-AZM, DSSAT and APSIM) was used to estimate yields of soybean and off-season maize for 28 locations in 12 states for a period of 34 years (1980–2013). Water deficit is the biggest problem for soybean and off-season maize crops in the regions of Cocos (state of Bahia), Buritis (Minas Gerais) and Formosa (G oiás), where the YGW accounted for ~70% of total YG. The YGM revealed that locations in the central region of Brazil, mainly in the state of Mato Grosso, presented an opportunity to increase yields of soybean and off-season maize, on average, by 927.5 and 909.6 5 kg ha–1, respectively. For soybean, YGM was the main cause of total YG in Brazil, accounting for 51.8%, whereas for maize, YGW corresponded to 53.8% of the total YG. Our results also showed that the choice of the best sowing date can contribute to reducing soybean YGW by 34–54% and off-season maize YGW by 66–89%. Brazilian agriculture has been able to increase its efficiency and reduce its expansion to new areas through cultivation of soybean and maize crops in the same area throughout the year. For soybean, water deficit was the main cause of total yield losses in Brazil, accounting for 51.8%, whereas for off-season maize, suboptimal management accounted for 53.8% of the total yield losses. The impacts of water deficit and suboptimal crop management varied across regions. [ABSTRACT FROM AUTHOR]

A unique feature of the International Plant Nutrition Colloquium is the range in scale of the sub-disciplines represented, from nanometers to global. Perhaps never in history has the Colloquium’s capacity to exploit this cross-cutting of scales been more important than it is today. Arguably, the single most important challenge for the field of plant nutrition is to contribute all it can to improving global productivity while at the same time increasing resource use efficiency. The global character of the demand for agricultural products and many of the most critical environmental issues creates a tight linkage between improving productivity and minimizing environmental impact. Sustainably meeting this challenge will require close cooperation and understanding among disciplines, across geographies, and between public and private sectors. Three concepts are offered that may facilitate this interaction. • The 4R Nutrient Stewardship Framework: Application of the right nutrient source, at the right rate, right time, and right place is a concept that when seen within a framework connecting practices to on-farm objectives and sustainability goals, along with critical performance indicators, can help keep individuals working on “parts” cognizant of the “whole”. • Mainstreaming of Simulation Models: Models recently developed can help identify unrealized yield potential and better manage the growing uncertainty of weather and climate. • Global Data Networks: More extensive exploitation of electronic technology that facilitates global data collection, sharing, analysis, and use could expedite the acquisition and application of agronomic and plant nutrition knowledge.

El Niño Southern Oscillation (ENSO) is one of the most important atmospheric-oceanic phenomena, responsible for climate variability in several Brazilian regions, which affects agriculture, mainly soybean – maize off-season succession. Therefore, the ENSO impacts on soybean – maize off-season double crop system can affect global food security, since Brazil is a major player as producer of these two crops, with a total production that represents 27% and 6% of world's soybean and maize production, respectively. In order to understand the risks associated to this crop system, the aim of this study was to assess the influence of ENSO phenomenon on the spatial and temporal soybean and maize off-season yield variabilities, considering simulations with three different crop models (FAO-AZM, DSSAT and APSIM) in a multi-model approach, and to determine the best sowing windows for this production system for each ENSO phase (El Niño, La Niña and Neutral) in different Brazilian producing regions. Previously calibrated and validated models were used to simulate soybean yields for 29 locations in 12 states, with sowing dates ranging from late September to early January of each growing season for a period of 34 years (1980–2013). The maize off-season sowing was done just after the soybean harvest, ranging from late January to early May. ENSO phases affected soybean and maize yields across the country, which can be minimized by choosing the best sowing window for soybean. In northern Brazil, El Niño negatively impacts soybean and maize off-season yields, making the succession of these crops risk, with the best sowing window being very short. Similar result was found for southern and central Brazil during La Niña years. On the contrary, cropping soybean and maize off-season in succession during El Niño years in center-south of and during La Niña years up north have higher chances of success. Unlabelled Image • The ENSO impacts varied according to the crop (soybean and maize), sowing date, location and ENSO phase. • The ENSO impacts can be minimized by choosing the best sowing window. • El Niño events negatively impacted soybean and maize yields in northern Brazil. • The soybean and maize yields were positively impacted under El Niño years in Central-South Brazil. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the author discusses various reports within the issue on topics including the plant and crop simulation models, and process-based crop simulation models (CSMs), and functional-structural plant models (FSPM).

Highlights • Sowing date can minimize the risks associated to soybean-maize succession in Brazil. • The optimal sowing date varied according to the Brazilian regions. • To maximize the net revenue in Central Brazil, the soybean sowing should be done in late September. • Drought, frosts and low solar radiation are the main causes of yield losses for soybean-maize succession in Brazil. Abstract The soybean-maize succession is an important production system used in Brazil. The greatest challenge related to this kind of system is to define the best sowing dates for the producing regions with different climatic characteristics, improving farmer´s economic profitability. Thus, the aim of this study was to determine the best sowing dates for the above-mentioned crop system considering simulations with three crop simulation models (FAO-AZM, DSSAT and APSIM) in a multi-model approach, and to determine the economic profitability of this system at national scale. Previously calibrated and validated models were used to simulate soybean yields for 29 locations in 12 states, with sowing dates ranging from end of September to beginning of January for a period of 34 years (1980–2013). The maize off-season sowing was done just after the soybean harvest, ranging from end of January to beginning of May. The yield data was converted to gross revenue according to the prices commonly practiced in Brazil and then to net revenue by subtracting the production costs for each assessed region. The optimal sowing date varied according to the Brazilian region. For Central Brazil, the highest net revenue was obtained when soybean was sown between the end of September and beginning of October. This period is also recommended in Southern Brazil, because sowing delay can reduce maize yield due to risks of frosts and low solar radiation availability. In the Northern Brazil, mainly in Pará state, the soybean sowing should start in November, when net revenue is maximized. [ABSTRACT FROM AUTHOR]

Water deficit is one of the main causes of sugarcane yield limitation around the world. Sugarcane simulation models can be used to evaluate the impact of irrigation on yield where water deficit causes important losses. The aim of this study was to dimension the potential of irrigation on sugarcane yield in different Brazilian regions, by comparing yields simulated by a calibrated FAO-AZM model under rainfed and irrigated conditions, considering different levels of crop water supply. Simulations were run for 12-month plant cane yield planted every month along the year. Three different soil types with low, medium and high soil water holding capacities were considered for 29 locations in Brazil, considering a 30-year weather data series. Irrigation was applied to cover 20, 40, 60, 80 and 100% of the crop water deficit, with the highest level corresponding to full irrigation to obtain potential yield. The results showed that yield increases depend on the interaction between climate type, weather conditions, soil physical properties, planting date and level of irrigation. The soil with low water holding capacity was the one with the largest response to irrigation. On the coast of Northeastern Brazil, sugarcane planted between March and June was those which were more responsive to irrigation. On the other hand, in the producing regions in the interior of the country, the planting dates with higher responses to irrigation were between October and January. This study proved that irrigation is an important strategy to improve sugarcane yield even in the regions where irrigation is currently not recommended. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The gridded database provided by National Aeronautics and Space Administration/Prediction of World Wide Energy Resources (NASA/POWER) presents a global coverage of complete weather data at horizontal resolution of 1° latitude–longitude, becoming a potential source for agrometeorological studies. Once Brazil is a country with continental dimensions and the major sugarcane world producer, and its density of ground weather stations suitable for an efficient agricultural planning is sparse, the objectives of this study were to test how robust is the NASA/POWER database through its comparison with the Brazilian ground weather stations network records (INMET) and to quantify the impacts on potential (Yp) and attainable (Yatt) sugarcane yield simulations when setting NASA/POWER as source of input weather data. The comparisons for weather data records and sugarcane yield simulations were carried out from 1997 to 2016. Statistical indices presented a satisfactory performance for average air temperature (R2 = 0.73; d = 0.91), minimum air temperature (R2 = 0.72; d = 0.91), maximum air temperature (R2 = 0.57; d = 0.84), solar radiation (SR) (R2 = 0.71; d = 0.92), sunshine hours (R2 = 0.68; d = 0.90) and reference evapotranspiration, when calculated through Priestley–Taylor (ETo-PT) method (R2 = 0.76; d = 0.93). When the weather variables were aggregated and compared with a 10-day time scale, a strong improvement of statistical indices was obtained. Yp presented root mean square error (RMSE) smaller than 10 t ha−1 while relative mean error (RME) ranged between ±10% for majority of grid cells, with exception for southern Brazil due to low and frost temperatures that satellite cannot capture accurately. Even NASA/POWER offering a relatively coarse grid size database and perhaps some regional data fitting would give better results at higher latitudes and elevation. The results found in this study proved that NASA/POWER products could be used as a source of climatic data for agricultural activities with a reasonable confidence for regional and national spatial scales. [ABSTRACT FROM AUTHOR]

The use of crop simulation models for interpreting experiments and analyzing production systems in different management and environmental conditions is common in the literature. In this study, parameterization and evaluation of the Simple Simulation Model (SSM) for the prediction of peanut ( Arachis hypogaea L.) growth and yield was conducted for the first time. Data from different field experiments from Astaneh Ashrafieh of northern Iran were used for coefficient estimation and model evaluation for the Virginia-type peanut variety North Carolina 2 (cv. NC2). After estimation of genetic parameters, the model was tested using independent data. The SSM simulated peanut growth and yield with reasonable accuracy, using data of more than 10 field experiments from different environmental conditions (11 experiments in the parameterization stage and 15 experiments in the evaluation stage). Based on data of independent experiments that were not used for parameterization, the model predicted an acceptable percentage of the observed results concerning days to harvest maturity (r = 0.46, CV = 5%), accumulated dry matter (r = 0.66, CV = 15%), grain yield (r = 0.55, CV = 21%), and pod yield (r = 0.45, CV = 18%). Local sensitivity analysis with 23 parameters indicated that two parameters related to leaf development and a parameter related to yield formation were the most sensitive cultivar-specific parameters; thus, estimation of the parameters need to be done with care for new cultivars. The SSM provided an adequate level of peanut growth simulation and based both on its transparency and easiness-to-use can be used as a valid tool for simulating growth of peanut Virginia-type varieties. [ABSTRACT FROM AUTHOR]

Waterlogging affects millions of hectares traditionally used for food production every year. Despite this, existing literature and process-based frameworks enabling simulation of waterlogging are sparse. Here, we reveal a lack of field experiments that have enumerated effects of waterlogging on plant growth. We call for more research on waterlogging, particularly in controlled field conditions with quantified soil properties and continuous monitoring of soil moisture. We opine that future experiments should explicitly focus on the impact of waterlogging on phenology, root development, and water and nutrient uptake, including interactions with atmospheric CO 2 concentration, temperature and other biotic/abiotic stresses. Such experimental data could then be used to develop waterlogging algorithms for crop models. Greater understanding of how waterlogging impacts on plant physiology will be conducive to more robust projections of how climate change will impact on global food security. • Existing process-based frameworks enabling simulation of waterlogging are sparse. • We advocate that waterlogging experiments in field conditions are needed. • More accurate projections of crop production under climate change need robust models. [ABSTRACT FROM AUTHOR]

Maintaining cereal production in the Bekaa valley in Lebanon presents a serious challenge. Lack of water is the driving force of agricultural research which is mainly focused on introduction of drought resistant cultivars, application of conservation tillage and supplemental irrigation. In this context forty-eight experimental plots were laid out for three years in a statistical split plot design. The statistical analyses showed that aboveground biomass and yield were significantly affected by irrigation for barley but not for the yield of durum wheat. Effects of soil tillage practices and introduction of new cultivares were not significant. A soil survey indicated that the implicit assumption of soil homogeneity of the agronomic design was correct for surface soil but that two different soil types (Cambisols and Fluvisols) had to be distinguished considering subsoil conditions and corresponding rooting patterns. Therefore, the main objective of this paper was to determine the effects of different soil types on crop response and, in addition, to assess how physically-based modeling can predict future effects of climate change on crops and soils. Simulation model SWAP was validated for local conditions using measurements of soil water contents, aboveground biomass and yield of wheat. Considering two rather than one soil type for the experimental area resulted in different conclusions for both crops as to the effectivity of both conservation tillage and irrigation, demonstrating that a distinction of only one soil type results in misleading results. The validated model was applied to estimate yields considering climate change, focusing on the application of supplemental irrigation. Yields for “Mikii3” a durum wheat cultivar are expected to increase by appr. 14% in both soils due to climate change. More importantly, only 3 supplemental irrigations would be needed for the deep soil requiring 5% more water as compared with current climate trend, while the shallow soil needs 13 irrigations, corresponding with a need for 35% more water. This is highly significant from an economic point of view and supports the relevance of distinguishing two soil types. It was demonstrated the synergy of joint research by the agronomic and soil science community and the need for executing a soil survey in future when planning agronomic experiments, including a hydrological soil characterisation. [ABSTRACT FROM AUTHOR]

The study was undertaken to assess the effect of environmental, management, and stress factors on nitrogen uptake patterns through the crops’ growth cycle and to associate temporal patterns of N uptake with biomass and grain yields. Existing complete experimental data, provided by several institutional databases and through an extensive literature review, were utilized together with crop simulation models (CSMs) to synthesize yield and N uptake profiles of the key staple cereal crops in selected agro-ecologies. Approximately 465 observations were identified for combined maize grain yield and plant N uptake: 156 for rice and 254 for wheat. The Decision Support System for Agrotechnology Transfer (DSSAT), which comprises CSMs and data that integrate capabilities on soils, daily weather, crops, and management, was used in combination with field information to first validate the CERES-maize, -rice, and -wheat models. The most noteworthy results from synthesis of the data set for the three key cereals were as follows: (a) N uptake continued to increase with time until physiological maturity with adequate N supply; (b) significant effect of soil N status on N uptake kinetics was observed at zero N; (c) N uptake profile was also influenced by the planting date, with the summer planting showing higher uptake than other planting dates; (d) field methods of N application influenced N uptake kinetics: a one-time injected or subsurface-applied urea continued to provide an adequate amount of N throughout the crop growth phase that was comparable or even higher than with broadcasting multiple splits; (e) N uptake was also dependent on crop cultivars, including stages of vegetative and reproductive phases, with shorter vegetative and longer reproductive phases showing continuous N uptake and lesser dependence on N remobilization; and (f) predictions suggested that modest changes in ambient temperature and atmospheric carbon dioxide (CO2) concentrations would not significantly alter the N uptake kinetics, with the uptake rate expected to increase under future climate change scenarios. The combined data suggest that no one N uptake kinetic pattern fits all crops under all environments and management practices. [ABSTRACT FROM PUBLISHER]

Climate change and its associated higher frequency and severity of adverse weather events require genotypic adaptation. Process-based ecophysiological modelling offers a powerful means to better target and accelerate development of new crop cultivars. Barley ( Hordeum vulgare L.) is an important crop throughout the world, and a good model for study of the genetics of stress adaptation because many quantitative trait loci and candidate genes for biotic and abiotic stress tolerance have been identified in it. Here, we developed a new approach to design future crop ideotypes using an ensemble of eight barley simulation models (i.e. APSIM, CropSyst, HERMES, MCWLA, MONICA, SIMPLACE, SiriusQuality , and WOFOST), and applied it to design climate-resilient barley ideotypes for Boreal and Mediterranean climatic zones in Europe. The results showed that specific barley genotypes, represented by sets of cultivar parameters in the crop models, could be promising under future climate change conditions, resulting in increased yields and low inter-annual yield variability. In contrast, other genotypes could result in substantial yield declines. The most favorable climate-zone-specific barley ideotypes were further proposed, having combinations of several key genetic traits in terms of phenology, leaf growth, photosynthesis, drought tolerance, and grain formation. For both Boreal and Mediterranean climatic zones, barley ideotypes under future climatic conditions should have a longer reproductive growing period, lower leaf senescence rate, larger radiation use efficiency or maximum assimilation rate, and higher drought tolerance. Such characteristics can produce substantial positive impacts on yields under contrasting conditions. Moreover, barley ideotypes should have a low photoperiod and high vernalization sensitivity for the Boreal climatic zone; for the Mediterranean, in contrast, it should have a low photoperiod and low vernalization sensitivity. The drought-tolerance trait is more beneficial for the Mediterranean than for the Boreal climatic zone. Our study demonstrates a sound approach to design future barley ideotypes based on an ensemble of well-tested, diverse crop models and on integration of knowledge from multiple disciplines. The robustness of model-aided ideotypes design can be further enhanced by continuously improving crop models and enhancing information exchange between modellers, agro-meteorologists, geneticists, physiologists, and plant breeders. [ABSTRACT FROM AUTHOR]

Crop simulation models allow analyzing various tillage-rotation combinations and exploring management scenarios. This study was conducted to test the DSSAT (Decision Support System for Agrotechnology Transfer) modelling system in rainfed semiarid central Spain. The focus is on the combined effect of tillage system and winter cereal-based rotations (cereal/legume/fallow) on the crop yield and soil quality. The observed data come from a 16-year field experiment. The CERES and CROPGRO models, included in DSSAT v4.5, were used to simulate crop growth and yield, and DSSAT-CENTURY was used in the soil organic carbon (SOC) and soil nitrogen (SN) simulations. Genetic coefficients were calibrated using part of the observed data. Field observations showed that barley grain yield was lower for continuous cereal (BB) than for vetch (VB) and fallow (FB) rotations for both tillage systems. The CERES-Barley model also reflected this trend. The model predicted higher yield in the conventional tillage (CT) than in the no tillage (NT) probably due to the higher nitrogen availability in the CT, shown in the simulations. The SOC and SN in the top layer only, were higher in NT than in CT, and decreased with depth in both simulated and observed values. These results suggest that CT-VB and CT-FB were the best combinations for the dry land conditions studied. However, CT presented lower SN and SOC content than NT. This study shows how models can be a useful tool for assessing and predicting crop growth and yield, under different management systems and under specific edapho-climatic conditions.

The risk to food security is particularly dire in rural sub-Saharan Africa, where a third of the world’s undernourished people reside. Intercropping has the potential to improve rural livelihoods through better resource utilization and improved resilience to current and future challenges. This paper reviewed the concepts in intercropping and outlined how resources are captured and utilized within the system. Crop simulation models (CSMs) as decision support tools for intercrop/multicrop systems and future directions for modelling multicrop systems are the focus of the review. Through increased crop biodiversity, intercropping improves resilience, food security and nutrition. This is achieved through improved resource capture and utilization due to differences in spatial and temporal distribution of component crops. For farmers to maximize on these advantages, they need to have a thorough knowledge of species combination, arrangements and proportions. A major drawback to intercrop systems is that most of the existing agronomic recommendations are tailored on monoculture practices. This is also evident in the structure of most CSMs that cannot account for heterogeneous crop stands. In conclusion, there is a need to enhance agricultural research on intercrop systems, combining conventional and modern research approaches. Moreover, CSMs should be multidimensional in order to simulate system diversity accurately. [ABSTRACT FROM PUBLISHER]

Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO 2 concentration, soil types, tillage, residues, intermediate or catch crops). We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO 2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations. The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects. [ABSTRACT FROM AUTHOR]

Use of seasonal and sub-seasonal forecast products of experimental extended range forecast system (ERFS) in crop models is investigated for improving the rice grain yield prediction skill for the ensuing monsoon season in the experimental station at Bhubaneswar, India. A stochastic disaggregation is used to downscale seasonal and monthly forecast products in daily weather sequences. These weather series are taken as input in Crop Estimation through Resource and Environment Synthesis (CERES)-rice crop simulation model for the crop yield prediction at different stages of forecast: June-September (4 month forecast), July-September (3 month forecast), August-September (2month forecast) and monthly forecast for September (1 month forecast). To avoid a technological trend in historical yield data, yields simulated with observed weather data have been used as the benchmark (observed rice yield) to evaluate the yields simulated using experimental ERFS forecasts. The findings recommend the efficiency of forecast products to capture year-to-year variability in observed rice yield at experimental stations. A significant enhancement in the prediction skill is noticed as the season advances due to incorporation of observed weather data, reducing uncertainty of yield prediction. The outcomes are useful for taking decisions well in advance for transplanting of rice as well as for other input management and farm activities during different stages of the crop growing season. [ABSTRACT FROM AUTHOR]

Linking decadal variability with short-term variability could be potentially exploited for improving seasonal climate forecasting for assisting crop management decisions. The objective of this study was to explore whether there are decadal variations in wheat (winter crop) and maize (summer crop) production and whether these decadal variations correlate with any known variations of climate. Over one hundred years of wheat and maize yields were simulated using process-based crop models with dynamically downscaled daily reanalysis data over four locations in the southeast USA. Using wavelet and cross-wavelet analysis, we found that winter crop yields were dominated by 10- and 22-year decadal oscillations; the decadal variations of winter crop yields were driven by decadal variations of winter temperature and spring precipitation; no decadal variations were detected for summer crop yields and summer precipitation and temperature. Cross-wavelet analysis showed that the decadal variations of winter crop yields were correlated with indices of the annual Atlantic Multi-decadal Oscillation (AMO), the annual Pacific Decadal Oscillation (PDO), and the winter North Atlantic Oscillation (NAO). Therefore, this knowledge of decadal climate variability could potentially be leveraged to predict winter seasonal yields of crops. [ABSTRACT FROM AUTHOR]

Nitrogen uptake and supply directly depends upon timing and method of application, soil physical conditions, climate and plant genetic features. Therefore, it varies with location and environment. Crop simulation models can be complementary decision support tools in field experiments to develop innovative crop management systems. APSIM (Agricultural Production Systems Simulator) is software which allows dynamic simulation of crop production, residue management, soil water and nutrient flow under different timing and methods of fertilizer application. In present studies, APSIM was calibrated and validated to predict nitrogen use efficiency of wheat under rain-fed conditions for Pothwar region of Pakistan. Field experiments were laid out using RCBD four factor factorial design replicated four times at PMAS-Arid Agriculture University, Rawalpindi Research area. Two wheat genotypes were planted during years (2010-11 and 2011-12) using different nitrogen rates [N0 (No fertilizer), N50 (50kg ha-1), N100 (100 kg ha-1) and N150 (150 kg ha-1)] and application methods by keeping individual plot size of 4m x 6m and row spacing of 25cm. Maximum nitrogen use efficiency (108.49 kg kg-1) was calculated for N0 while minimum NUE (25.47 kg kg-1) calculated for N150. Split dose application method gave more NUE (58.95 kg kg-1) than full dose nitrogen application method (53.77 kg kg-1). Genotype NARC-2009 performed better and gave maximum NUE (60.55 kg kg-1), while minimum NUE (52.17 kg kg-1) was calculated for Chakwal-50. Similarly, during 2010-11, more NUE (60.19 kg kg-1) was calculated than 2011-12 (50.52 kg kg-1). Days to maturity, biomaβ nitrogen, grain yield and grain nitrogen were recorded from the field experiment as well as simulated by APSIM model. The simulated outcomes for all these parameters were strongly correlated. The simulation depicted a strong dependency of the mineral nitrogen concentration upon plant nitrogen uptake and growth. The validation skill scores like R2 and RMSE confirmed the ability of APSIM to model nitrogen use efficiency in wheat under rain-fed conditions. Therefore, simulation modeling approaches should be adopted to recommend optimum fertilizer dose and timing to get maximum crop yield and eliminate nitrogen loβes in the context of extreme climate variability. © 2014 Friends Science Publishers [ABSTRACT FROM AUTHOR]