Efficacy of planting date adjustment as a cultivation strategy to cope with drought stress and increase rainfed maize yield and water-use efficiency

Lu, Hsiu-Ying; Xue, Jiquan; Guo, Dongwei

doi:10.1016/j.agwat.2016.09.001

Cited by 86 publications

(45 citation statements)

References 25 publications

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“…This indicates that DCN can recognize the key growth phases during the corn growing season through the temporal learning module. Previous studies have demonstrated that corn yield is highly correlated to the rainfall before silking and effective accumulated temperature after silking (Lu et al 2017). The variability of attention values starts increasing from week 15.…”

Section: Temporal Pattern Learning Module By At-lstmmentioning

confidence: 93%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

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Large-scale crop yield estimation is critical for understanding the dynamics of global food security. Understanding and quantifying the temporal cumulative effect of crop growth and spatial variances across different regions remains challenging for large-scale crop yield estimation. In this study, a deep spatial-temporal learning framework, named DeepCropNet (DCN), has been developed to hierarchically capture the features for county-level corn yield estimation. The temporal features are learned by an attention-based long short-term memory network and the spatial features are learned by the multi-task learning (MTL) output layers. The DCN model has been applied to quantify the relationship between meteorological factors and the county-level corn yield in the US Corn Belt from 1981 to 2016. Three meteorological factors, including growing degree days, killing degree days, and precipitation, are used as time-series inputs. The results show that DCN provides an improved estimation accuracy (RMSE=0.82 Mg ha −1 ) as compared to that of conventional methods such as LASSO (RMSE=1.14 Mg ha −1 ) and Random Forest (RMSE=1.05 Mg ha −1 ). Temporally, the attention values computed from the temporal learning module indicate that DCN captures the temporal cumulative effect and this temporal pattern is consistent across all states. Spatially, the spatial learning module improves the estimation accuracy based on the regional specific features captured by the MTL mechanism. The study highlights that the DCN model provides a promising spatial-temporal learning framework for corn yield estimation under changing meteorological conditions across large spatial regions.

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Section: Temporal Pattern Learning Module By At-lstmmentioning

confidence: 93%

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Lin

Zhong

Wang

et al. 2020

Environ. Res. Lett.

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“…Water stress is a key factor that restricts grain yield in areas with limited irrigation, and PD mainly affects yield formation by regulating the reasonable correspondence between rainfall and GP [11]. However, in areas where chilling injury frequently occurs, the selected PD should fully use photo-thermal resources for the limited accumulated temperature [13].…”

Section: Discussionmentioning

confidence: 99%

“…In cold middle-latitude regions, the limitation of PD delay should be based on the required accumulated temperature for crop growth [9]. Maize growth is accelerated with PD postponement, leading to a shortening growth period and an adverse effect on biomass accumulation [10,11]. Moreover, delayed sowing increases the probability of early autumn freezing occurring at latter stages in maize growth, thereby restraining grain maturity and leading to output reduction [12,13].…”

mentioning

confidence: 99%

Effect of Planting Date on Accumulated Temperature and Maize Growth under Mulched Drip Irrigation in a Middle-Latitude Area with Frequent Chilling Injury

Wang

et al. 2017

Sustainability

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Given that chilling injury, which involves late spring cold and early autumn freezing, significantly affects maize growth in middle-latitude cold areas, a highly efficient cultivation technique combining suitable planting date (PD) and mulched drip irrigation is being studied to guarantee maize production. A field experiment for medium-mature variety "Xianyu 335" was conducted in 2015 to 2016 in Chifeng, Inner Mongolia, China, to explore the effects of PD on the active accumulated temperature (AAT) distribution and maize growth under mulched drip irrigation. Based on the dates (around May 1) of late spring cold occurring in the area, four PDs were designed, namely, April 20 (MD 1 ), May 2-3 (MD 2 ), May 12 (MD 3 ), and May 22 (MD 4 ), and a non-film mulching treatment (NM-D 2 ) was added on the second PD. Results indicated that: (1) the warming effect of film mulching effectively compensated for the lack of heat during the early stages of maize growth. Compared with that in NM-D 2 , the soil temperature under mulching in MD 2 for the sowing-emergence and seedling stage increased by 14.3% and 7.6%, respectively, promoting maize emergence 4 days earlier and presenting 5.6% and 9.7% increases in emergence rate and grain yield, respectively; (2) the AAT reduction caused by PD delay was mainly observed in reproductive stage, which reached 96.6 • C for every 10 days of PD delay in this stage; (3) PD markedly affected maize growth process and yield, which were closely related to the chilling injury. The late spring cold slowed down the emergence or jointing for maize (under MD 1 and MD 2 ), but brought insignificant adverse effect on maize later growth and grain yield (16.1 and 15.9 Mg·ha −1 , respectively). While the maize in both MD 3 and MD 4 treatments suffered from early autumn freezing damage at the anthesis-maturity stages, resulting in shortening in reproductive period by 4-8 days and decrease in grain yield by 11.4-17.3% compared with those in MD 1 and MD 2 ; and (4) taking the typical date (May 1) of late spring cold occurring as the starting point, the grain yield penalty reached 8.5% for every 10 days of PD delay; for every 100 • C of AAT decrease during reproductive stage, the grain yield decreased by 6.1%. The conclusions offer certain reference values for maize cultivation in the same latitude areas with similar ecological environments.

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“…Other agronomy practices such as intercropping maize with crops requiring less water (e.g. peanut), cultivar selection, adjusting sowing windows (Liu et al, 2013;Lu et al, 2017) and ridge-furrow with covering plastic film (Dong et al, 2017) are likely more applicable in optimizing crop yield and regional sustainability. Our study provides more evidence to understand crop responses to water stress, especially in relation to root morphological plasticity in a drought environment.…”

Section: Discussionmentioning

confidence: 99%

Morphological plasticity of root growth under mild water stress increases water use efficiency without reducing yield in maize

et al. 2017

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Abstract. A large yield gap exists in rain-fed maize (Zea mays L.) production in semi-arid regions, mainly caused by frequent droughts halfway through the crop-growing period due to uneven distribution of rainfall. It is questionable whether irrigation systems are economically required in such a region since the total amount of rainfall does generally meet crop requirements. This study aimed to quantitatively determine the effects of water stress from jointing to grain filling on root and shoot growth and the consequences for maize grain yield, above-and below-ground dry matter, water uptake (WU) and water use efficiency (WUE). Pot experiments were conducted in 2014 and 2015 with a mobile rain shelter to achieve conditions of no, mild or severe water stress. Maize yield was not affected by mild water stress over 2 years, while severe stress reduced yield by 56 %. Both water stress levels decreased root biomass slightly but shoot biomass substantially. Mild water stress decreased root length but increased root diameter, resulting in no effect on root surface area. Due to the morphological plasticity in root growth and the increase in root / shoot ratio, WU under water stress was decreased, and overall WUE for both above-ground dry matter and grain yield increased. Our results demonstrate that an irrigation system might be not economically and ecologically necessary because the frequently occurring mild water stress did not reduce crop yield much. The study helps us to understand crop responses to water stress during a critical water-sensitive period (middle of the crop-growing season) and to mitigate drought risk in dry-land agriculture.

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Efficacy of planting date adjustment as a cultivation strategy to cope with drought stress and increase rainfed maize yield and water-use efficiency

Cited by 86 publications

References 25 publications

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

DeepCropNet: a deep spatial-temporal learning framework for county-level corn yield estimation

Effect of Planting Date on Accumulated Temperature and Maize Growth under Mulched Drip Irrigation in a Middle-Latitude Area with Frequent Chilling Injury

Morphological plasticity of root growth under mild water stress increases water use efficiency without reducing yield in maize

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