Mineral nitrogen fertilization and stover management effects on maize production under irrigated mediterranean conditions. Simulation of yields

The optimal N application rates were investigated with the effects of stover management on maize production and its possible interaction with N fertilization. A field experiment was conducted from 2010 to 2014. The rates of mineral N fertilization applied were: 0, 100, 200 and 300 kg N ha-1 year-1. Our results suggested that grain yield, biomass, grain and plant N uptake and SPAD-units were all greatly affected by N fertilization rates. Maximum yield values (19.93 and 19.20 Mg ha−1) were achieved with N application rates of 200 kg ha-1. Our results suggested that returning stover to the soil over a period of five years had a positive impact on SOC (soil organic matter) levels, without any yield penalties. We evaluated the performance of the CSM–CERES and CSM-IXIM maize models in their DSSAT to simulate high yielding conditions and we also tested the IXIM model using an alternative approach

Using CERES-maize and ENSO as decision support tools to evaluate climate-sensitive farm management practices for maize production in the northern regions of Ghana

Open Access JournalMaize (Zea mays) has traditionally been a major cereal staple in southern Ghana. Through breeding and other crop improvement efforts, the zone of cultivation of maize has now extended to the northern regions of Ghana which, hitherto, were the home to sorghum and millet as the major cereals. Maize yield in the northern Ghana is hampered by three major biophysical constraints, namely, poor soil fertility, low soil water storage capacity and climate variability. In this study we used the DSSAT crop model to assess integrated water and soil management strategies that combined the pre-season El-Niño-Southern Oscillation (ENSO)-based weather forecasting in selecting optimal planting time, at four locations in the northern regions of Ghana. It could be shown that the optimum planting date for a given year was predictable based on February-to-April (FMA) Sea Surface Temperature (SST) anomaly for the locations with R2 ranging from 0.52 to 0.71. For three out of four locations, the ENSO-predicted optimum planting dates resulted in significantly higher maize yields than the conventional farmer selected planting dates. In Wa for instance, early optimum planting dates were associated with La Nina and El Niño (Julian Days 130-150; early May to late May) whereas late planting (mid June to early July) was associated with the Neutral ENSO phase. It was also observed that the addition of manure and fertilizer improved soil water and nitrogen use efficiency, respectively, and minimized yield variability, especially when combined with weather forecast. The use of ENSO-based targeted planting date choice together with modest fertilizer and manure application has the potential to improve maize yields and also ensure sustainable maize production in parts of northern Ghana

Soil organic carbon dynamics and crop yield for different crop rotations in a degraded ferruginous tropical soil in a semi-arid region: a simulation approach

In recent years, simulation models have been used as a complementary tool for research and for quantifying soil carbon sequestration under widely varying conditions. This has improved the understanding and prediction of soil organic carbon (SOC) dynamics and crop yield responses to soil and climate conditions and crop management scenarios. The goal of the present study was to estimate the changes in SOC for different cropping systems in West Africa using a simulation model. A crop rotation experiment conducted in Farakô-Ba, Burkina Faso was used to evaluate the performance of the cropping system model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT) for simulating yield of different crops. Eight crop rotations that included cotton, sorghum, peanut, maize and fallow, and three different management scenarios, one without N (control), one with chemical fertilizer (N) and one with manure applications, were studied. The CSM was able to simulate the yield trends of various crops, with inconsistencies for a few years. The simulated SOC increased slightly across the years for the sorghum–fallow rotation with manure application. However, SOC decreased for all other rotations except for the continuous fallow (native grassland), in which the SOC remained stable. The model simulated SOC for the continuous fallow system with a high degree of accuracy normalized root mean square error (RMSE)=0·001, while for the other crop rotations the simulated SOC values were generally within the standard deviation (s.d.) range of the observed data. The crop rotations that included a supplemental N-fertilizer or manure application showed an increase in the average simulated aboveground biomass for all crops. The incorporation of this biomass into the soil after harvest reduced the loss of SOC. In the present study, the observed SOC data were used for characterization of production systems with different SOC dynamics. Following careful evaluation of the CSM with observed soil organic matter (SOM) data similar to the study presented here, there are many opportunities for the application of the CSM for carbon sequestration and resource management in Sub-Saharan Africa

Sequestro potenziale di carbonio in sistemi colturali cerealicoli della collina marchigiana

Una delle sfide più importanti dell’agricoltura di questi anni è lo sviluppo di pratiche colturali in grado di aumentare il sequestro di carbonio organico nel suolo (Purakayastha et al. 2008). La scelta del sistema colturale e in particolare l’utilizzo di una bilanciata gestione della fertilizzazione può influenzare in misura molto rilevante il potenziale di incremento del carbonio organico nei suoli agrari (Lal, 2002). Il lavoro aveva l’obiettivo di valutare, attraverso sperimentazioni di campo e modelli di simulazione, l’impatto sulla sostanza organica del suolo di diverse modalità di fertilizzazione azotata nell’ambito di un avvicendamento asciutto frumento duro-mais della collina marchigiana

Parameterization of CROPGRO-soybean model and its use as a tool to assess the impact of climate change on the soybean crop

Antes de su uso para la toma de decisiones, los modelos de cultivos deben ser calibrados con datos de campo de la región en la cual serán utilizados. Los objetivos del trabajo fueron calibrar y validar la capacidad predictiva del modelo CROPGRO-soybean. Se utilizaron datos de dos cultivares del grupo de madurez IV (Asgrow 4656 y Don Mario DM4700) y tres años de experimentos de campo en condiciones no limitantes en Azul, Buenos Aires, Argentina. La calibración comenzó con los coeficientes del grupo IV que por defecto se encuentran en los archivos: especie, ecotipo y cultivar. Modificaciones menores fueron hechas para ajustar la fenología y dinámica del crecimiento para ambos cultivares. Con el archivo especie original la materia seca, el aumento del número de vainas y el crecimiento de las mismas fue subestimado. La temperatura base cardinal para la fotosíntesis y formación de vainas se redujeron, con estas modificaciones se obtuvieron buenas predicciones para crecimiento y rendimiento. Con el CROPGRO-soybean calibrado y utilizando las proyecciones para la región del modelo climático regional PRECIS bajo el escenario SRESA2 en los años 2030 y 2060, se evaluaron los efectos del cambio climático global futuro en los rendimiento del cultivo de soja. Bajo esos escenarios y en condiciones de secano, se prevén aumentos del rendimiento de 34 y 38% para cada uno de los años estudiados, con una leve mejora atrasando la fecha de siembra respecto de la óptima actual. • rendimiento • sojaPrior to their use in decision-making, crop models need to be calibrated with field data from the region where the model will be used. The objectives of this research were calibrate and validate the predictive ability of CROPGRO-soybean model. Data from two cultivars maturity group IV (Asgrow 4656 and Don Mario DM4700) and three years of field experiments in conditions not limiting in Azul, Buenos Aires, Argentina were used. Calibration started with the coefficients of group IV that by default are in files: species, ecotype and cultivate. With the original species file, dry matter, increasing the number of pods and growth were underestimated. Minor changes in file were made to adjust phenology and growth dynamics for both cultivars. The cardinal base temperatures for photosynthesis and pod formation were reduced; with these modifications good predictions for growth and yield were obtained. With the CROPGRO-soybean calibrated and using projections for the region from PRECIS regional climate model under the SRESA2 scenario in the years 2030 and 2060, the effects of global climate change in future soybean crop yield were evaluated. Under these scenarios and rainfed conditions, are anticipated yield increases of 34 and 38% for each of the years studied with a slight improvement delaying the planting date, respect to current optimum date.Fil: Confalone, Adriana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Vilatte, Carlos. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Lázaro, Laura. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Roca, Núria. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Mestelan, Silvia. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Aguas, Laura. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Navarro, Miguel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de AgronomíaFil: Sau, Federico. Universidad Politécnica de Madrid. Departamento de Biología Vegeta

Simulating the effect of tillage practices on the yield production of wheat and barley under dryland condition

In arid and semiarid regions, soil tillage practices have major effects on soil water dynamics. In this study, we compared the effects of Zero tillage (ZT) and Conventional tillage (CT) on the grain yield of rainfed barley and wheat at three locations i.e. Barrani, El-Neguilla and Matrouh in the north western coast of Egypt. We also tested the performance of the DSSAT (Decision Support System for Agrotechnology Transfer). In the first season of 2017/2018, only barley plants in Barrani location were able to grow and produce yield due to insufficient rain. Results showed that ZT produced significantly higher grain yield (almost 200%) for barley as compared to the CT treatment. In the second season of 2018/2019, conventional tillage produced higher yields as compared to the zero tillage treatment over the three studied locations and for the two crops. The DSSAT model successfully simulated the grain yield, total biomass and harvest index with an excellent agreement between simulated and observed data with NSE values of 0.868 and 0.800 for grain yield and total biomass respectively and a satisfactory agreement with NSE of 0.431 in case of harvest index. Tillage had a noticeable impact on grain yield of barley and wheat and the DSSAT successfully simulated the effects of the tillage treatments

Uncertainties in Simulating Crop Performance in Degraded Soils and Low Input Production Systems

Many factors interact to determine crop production. Cropping systems have evolved or been developed to achieve high yields, relying on practices that eliminate or minimize yield reducing factors. However, this is not entirely the case in many developing countries where subsistence farming is common. The soils in these countries are mainly coarse-textured, have low water holding capacity, and are low in fertility or fertility declines rapidly with time. Apart from poor soils, there is considerable annual variability in climate, and weeds, insects and diseases may damage the crop considerably. In such conditions, the gap between actual and potential yield is very large. These complexities make it difficult to use cropping system models, due not only to the many inputs needed for factors that may interact to reduce yield, but also to the uncertainty in measuring or estimating those inputs. To determine which input uncertainties (weather, crop or soil) dominate model output, we conducted a global sensitivity analysis using the DSSAT cropping system model in three contrasting production situations, varying in environments and management conditions from irrigated high nutrient inputs (Florida, USA) to rainfed crops with manure application (Damari, Niger) or with no nutrient inputs (Wa, Ghana). Sensitivities to uncertainties in cultivar parameters accounted for about 90% of yield variability under the intensive management system in Florida, whereas soil water and nutrient parameters dominated uncertainties in simulated yields in Niger and Ghana, respectively. Results showed that yield sensitivities to soil parameters dominated those for cultivar parameters in degraded soils and low input cropping systems. These results provide strong evidence that cropping system models can be used for studying crop performance under a wide range of conditions. But our results also show that the use of models under low-input, degraded soil conditions requires accurate determination of soil parameters for reliable yield predictions

Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation

Soil respiration represents the second largest CO2 flux from terrestrial ecosystems to the atmosphere, and a small rise could significantly contribute to further increase in atmospheric CO2. Unfortunately, the extent of this effect cannot be quantified reliably, and the outcomes of experiments designed to study soil respiration remain notoriously unpredictable. In this context, the mathematical simulations described in this article suggest that assumptions of linearity and presumed irrelevance of micro-scale heterogeneity, commonly made in quantitative models of microbial growth in subsurface environments and used in carbon stock models, do not appear warranted. Results indicate that microbial growth is non-linear and, at given average nutrient concentrations, strongly dependent on the microscale distribution of both nutrients and microbes. These observations have far-reaching consequences, in terms of both experiments and theory. They indicate that traditional, macroscopic soil measurements are inadequate to predict microbial responses, in particular to rising temperature conditions, and that an explicit account is required of microscale heterogeneity. Furthermore, models should evolve beyond traditional, but overly simplistic, assumptions of linearity of microbial responses to bulk nutrient concentrations. The development of a new generation of models along these lines, and in particular incorporating upscaled information about microscale processes, will undoubtedly be challenging, but appears to be key to understanding the extent to which soil carbon mineralization could further accelerate climate change

Identifying irrigation and nitrogen best management practices foraerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Research based development of best management options for aerobic rice–maize cropping systems must be developed to improve water and nitrogen use efficiency. The main objective of this study was to identify water saving rice production technology for rice grown in sandy loam soils in semi-arid conditions using the calibrated CERES-Rice and Maize models of the Decision Support System for Agro Technology Transfer (DSSAT). A two-year experiment with two different crop establishment methods viz., aerobic rice and flooded rice with four nitrogen rates followed by maize under zero tilled conditions was used to calibrate and evaluate DSSAT CERES-Rice and CERES-Maize models. The calibrated models were used to develop best management options for an aerobic rice–maize sequence which can produce similar yields with water savings relative to that of traditional flooded rice–maize system. The results showed that application of 180 kg N ha−1 in four splits and automatic irrigation with 40 mm, when soil available water (ASW) in top 30 cm fell below to 60% was the best management combination for aerobic rice, saving 41% of water while producing 96% of the yield attainable under flooded conditions. Similarly for maize, application of 120 kg N ha−1 and irrigation with 30 mm of water at 40% ASW in the top 30 cm soil was the most dominant management option. Further, application of 180 kg N ha−1 with rice followed by 120 kg N ha−1 in maize provided stable yield for both aerobic and flooded rice systems over time as simulated by the model. The results illustrate that DSSAT model is a useful tool for evaluating alternative management options aimed at maintaining yields and saving water in rice–maize systems in semi-arid regions