Yield responses of crops to changes in environment and management practices: Model sensitivity analysis. I. Maize

YIELD, a parametric crop production model, employs climatic data to calculate actual and potential yield for various crops and includes formulations for specific crop and growth stage effects. The objective was to demonstrate the sensitivity of YIELD for grain corn (maize) to changes in various environmental and decision-making inputs. Five temperature, five solar radiation, six relative humidity regimes, five water application schemes, and four irrigation frequencies were included in this study. The effects of different soil types and wind regimes on crop water requirements were investigated. The model output includes crop yield, water use efficiency, and management efficiency. Among the results, yield decreased on the average by 3.9% per one degree (C) increase in air temperature. A 1% change in solar radiation resulted in an average of 1% change in yield. Similar changes in relative humidity caused a yield change of about 0.8%.     

Application of a progressive-difference method to identify climatic factors causing variation in the rice yield in the Yangtze Delta, China

Time series of rice yields consist of a technology-driven trend and variations caused by climate fluctuations. To explore the relationship between yields and climate, the trend and temporal variation often have to be separated. In this study, a progressive-difference method was applied to eliminate the trend in time series. By differentiating yields and climatic factors in 2 successive years, the relationship between variations in yield and climatic factors was determined with multiple- regression analysis. The number of hours of sunshine, the temperature and the precipitation were each defined for different intervals during the growing season and used as different regression variables. Rice yields and climate data for the Yangtze Delta of China from 1961 to 1990 were used as a case study. The number of hours of sunshine during the tillering stage and the heading to milk stage particularly affected the yield. In both periods radiation was low. In the first period, the vegetative organs of the rice crop were formed while in the second period solar radiation was important for grain filling. The average temperature during the tillering to jointing stage reached its maximum, which affected rice yields negatively. Precipitation was generally low during the jointing and booting stages, which had a positive correlation with yield, while high precipitation had a negative effect during the milk stage. The results indicate that the climatic factors should be expressed as 20- to 30-day averages in the Yangtze Delta; a shorter or longer period, e.g. 10 or 40 days, is less appropriate. Received: 30 May 2000 / Revised: 27 October 2000 / Accepted: 30 October 2000     

水旱轮作系统作物养分管理策略

水旱轮作系统是我国主要的作物生产系统之一,主要分布在长江流域.作物和土壤季节间的干湿交替变化是这一系统的显著特征,这也引起了土壤物理、化学和生物学特性在不同作物季节间的交替变化,构成独特的农田生态系统.该系统面临的主要问题包括：生产力下降或徘徊不前,灌溉水日益短缺,养分管理不合理,资源利用效率低和环境污染等.本文在综述水旱轮作系统特征和存在问题的基础上,进一步提出通过养分资源综合管理策略解决该系统养分投入、作物生产和环境风险之间的矛盾.该策略的核心内容是：从整个轮作系统角度出发调控养分,综合应用各种养分资源（化肥、有机肥及环境养分）,使养分供应匹配作物需求,并根据不同养分资源特点采取相应的管理技术,使养分管理与节水、高产栽培等农作技术相结合.     

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.     

基于生态需水保障的农业生态补偿标准

面向流域农业需水和生态需水间的矛盾问题和协调发展的要求,提出了基于生态需水保障的农业生态补偿标准计算方法。其中考虑农业用水定额计算基于生态需水保障的农业用水短缺,引入水分生产函数模型建立保障生态需水量产生的农业用水短缺与产量损失间的关系,根据不同季节作物产量响应系数的变化,定量确定具有时间和等级差异性的农业生态补偿标准。以保障黄河口生态需水引起的山东引黄灌区农业损失补偿标准分析为实例,计算了冬小麦和夏玉米种植户不同等级的生态补偿标准。结论认为,农业生态补偿标准需根据不同的来水过程及生态需水等级确定,面积稳定和保障功能显著的粮食作物应作为补偿标准计算的依据。     

Mind the gap: how do climate and agricultural management explain the ‘yield gap’ of croplands around the world?

Aim As the demands for food, feed and fuel increase in coming decades, society will be pressed to increase agricultural production – whether by increasing yields on already cultivated lands or by cultivating currently natural areas – or to change current crop consumption patterns. In this analysis, we consider where yields might be increased on existing croplands, and how crop yields are constrained by biophysical (e.g. climate) versus management factors. Location This study was conducted at the global scale. Methods Using spatial datasets, we compare yield patterns for the 18 most dominant crops within regions of similar climate. We use this comparison to evaluate the potential yield obtainable for each crop in different climates around the world. We then compare the actual yields currently being achieved for each crop with their ‘climatic potential yield’ to estimate the ‘yield gap’. Results We present spatial datasets of both the climatic potential yields and yield gap patterns for 18 crops around the year 2000. These datasets depict the regions of the world that meet their climatic potential, and highlight places where yields might potentially be raised. Most often, low yield gaps are concentrated in developed countries or in regions with relatively high‐input agriculture. Main conclusions While biophysical factors like climate are key drivers of global crop yield patterns, controlling for them demonstrates that there are still considerable ranges in yields attributable to other factors, like land management practices. With conventional practices, bringing crop yields up to their climatic potential would probably require more chemical, nutrient and water inputs. These intensive land management practices can adversely affect ecosystem goods and services, and in turn human welfare. Until society develops more sustainable high‐yielding cropping practices, the trade‐offs between increased crop productivity and social and ecological factors need to be made explicit when future food scenarios are formulated.     

不同空间尺度下的ALMANAC模型验证

ALMANAC模型最早作为EPIC模型的一部分,用于模拟土壤侵蚀导致的土地生产力的下降．它将试验数据的统计过程和作物生长的机理过程结合起来,是一种典型的基于过程模拟的应用型作物生长模型．如能在不同的空间尺度上验证模型的适用性,无疑会大大扩展模型的应用范围．从这一目的出发,利用美国得克萨斯州19个试验田和9个县的玉米和高粱产量资料及其相关的作物、土壤、田问管理等数据,模拟了1998年田间尺度,1989～1998年县级尺度的平均作物产量．模拟结果表明,ALMANAC模型能够很好地模拟两种不同空间尺度的作物产量,其相对误差在田问尺度上分别为8．9％(高粱)和9．4％(玉米),在县级尺度上分别达到2．6％(玉米)和—0．6％(高粱)．该模型在进行产量预测、掌握作物生长动态,指导农业生产管理和土地利用等方面具有很好的应用前景．     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

气候变化背景下中国南方地区季节性干旱特征与适应Ⅳ.基于作物水分亏缺指数的玉米干旱时空特征

基于1959－2008年中国南方地区249个气象台站的地面观测资料,以作物水分亏缺指数为玉米干旱指标,计算其干旱频率和干旱站次比,分析中国南方地区春玉米和夏玉米各生育阶段发生干旱的时空分布特征.结果表明: 从干旱发生频率的空间分布看,春玉米在淮北、云南北部和华南南部发生的干旱较严重,在其他地区的干旱相对较轻;除了长江中下游地区、华南北部和西南东部的夏玉米在生育后期干旱较严重,研究区域内夏玉米在其生育前期和中期干旱较轻.从干旱面积和强度的变化趋势看,长江中下游地区春玉米在七叶到拔节阶段的干旱强度明显增加,在吐丝后到乳熟阶段的干旱面积和强度呈减小趋势;夏玉米在拔节后期到抽雄阶段以及吐丝后到乳熟阶段的干旱都呈减轻趋势.西南地区春玉米、夏玉米的干旱强度和范围没有明显趋势.从干旱面积和范围的年际和年代际变化看,长江中下游地区夏玉米的变化较大,而西南地区差异较小.     

Modeling soil water regime and corn yields considering climatic uncertainty

Real time estimation of soil moisture and crop yield plays an important role for best irrigation management practices especially in arid and semiarid regions. A simulation model able of real time estimating and forecasting soil water storage and corn yield response to soil moisture was developed by combining two existing models. Soil water storage was estimated through the soil water balance equation considering the uncertainty of evapotranspiration and combing with Kalman filter technique. Crop dry matter and grain yield were simulated by using a functional relationship between yield and soil moisture. Some improvements have been made in the response function by considering different impacts of moisture stress on crop growth and yield for the different growing stages. Four years data sets collected in an experimental station in the North China Plain were used to calibrate and test the model. Results indicate that soil moisture storage in the soil profile estimated and predicted by the model agrees well with the measured data, and the relative error of yield prediction is around 10%, which means that the combined model and the methodology applied are capable of predicting crop yield and soil water storage dynamics.