Drivers of spatial and temporal variation in soybean yield and irrigation requirements in the western US Corn Belt

Grassini, Patricio; Torrion, Jessica A.; Cassman, Kenneth G.; Yang, Haishun; Specht, James E.

doi:10.1016/j.fcr.2014.04.005

Cited by 55 publications

(38 citation statements)

References 38 publications

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“…In 2013, the highest yield of finger millet was obtained when planted on the plot of the precursor Boshe variety, which received 3 t FYM/ha. This result indicates that during the short rainy season, the early maturing soybean type produces biomass that contributes to soil organic matter through litter fall drops (2012) and hence to soil fertility improvement [12]). Moreover, this result is in line The interaction of residual effects of soybean varieties, due to nitrogen fixation and crop litterfall, and the application of FYM significantly influenced the yield of the succeeding finger millet (Figure 7).…”

Section: Finger Millet Performance Due To Residual Effectsmentioning

confidence: 98%

“…In 2013, the highest yield of finger millet was obtained when planted on the plot of the precursor Boshe variety, which received 3 t FYM/ha. This result indicates that during the short rainy season, the early maturing soybean type produces biomass that contributes to soil organic matter through litter fall drops (2012) and hence to soil fertility improvement [12]). Moreover, this result is in line with other research findings indicated that the lower rates of applied FYM also more easily decompose during the short rainy seasons and release some nutrients for the next crops as compared with the higher doses [11].…”

Section: Finger Millet Performance Due To Residual Effectsmentioning

confidence: 98%

“…The significant role and spatial variability of nitrogen and phosphorus highly affected crop responses [6]. In addition, appropriate rainfall amount and distribution not only enhance the yield, but also reduce year to year variability [12].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Organic and Inorganic Fertilizers on the Yield of Two Contrasting Soybean Varieties and Residual Nutrient Effects on a Subsequent Finger Millet Crop

Abebe

Deressa²

2017

Agronomy

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The problems of low soil fertility resulting from continuous monocropping, crop residue removal and limited fertilizer use represent key challenges to produce surplus food for the ever increasing population of Ethiopia. However, the practices of crop rotation and integrated sources of fertilizer uses could potentially improve soil fertility and productivity. In 2012 and 2014, soybean with different trials consisting of two soybean varieties (Boshe and Ethio-ugozilavia), three levels of farm yard manure (FYM) (3, 6 and 9 t/ha) and three phosphorus levels (8, 16 and 24 kg P ha −1 ) were combined in 2 × 3 × 3 factorial arrangements. Two soybean varieties receiving no fertilizer application followed by finger millet receiving a recommended rate (20 kg P/ha) were included. The experiment was laid out in a randomized complete block design with three replications. In 2013 and 2015, finger millet was planted on each soybean plot as per previous treatment arrangements to evaluate the effect of the precursor crop (soybean) and integrated fertilizer application on yield performance of the subsequent finger millet. Soil pH, organic carbon, total nitrogen and available phosphorus before planting and after crop harvest of soybean in each year showed treatment differences. Both precursor crop and fertilizer application had a positive effect on soil fertility status and, hence, improved the performance of the subsequent finger millet. On the other hand, since the rainfall amount and distribution were different in the 2012 and 2014 seasons, the response of soybean varieties to applied fertilizers was significantly affected, and the correlation between soybean yield and annual rainfall was strongly positive. Use of an early maturing soybean variety (Boshe) with the lowest rates of organic and inorganic fertilizers gave significantly higher yield in 2012 (short rainy season) compared with other treatment combinations. In the 2014 cropping season, however, 'Ethio-ugozilavia' showed greater yield performance with the combined application of 3 t FYM/ha and 1616 kg PP/ha followed by 3 t FYM and 88 kg P/ha. Hence, it is recommended to use the 'Boshe' variety under a short rainy season and under a low soil fertility status, while variety the 'Ethio-ugozilavia' can be used under good rainy and soil fertility management conditions. Considering residual effects, the use of the early maturing soybean variety as a precursor with 3 t FYM/ha and 8-16,816 kg P/ha during the short rainy season could enhance the yield of the subsequent finger millet. On the other hand, the use of the late maturing soybean variety as a precursor with higher organic fertilizer rates (6-9 t FYM/ha) resulted in a significant yield increase of the subsequent finger millet. The use of a late maturing variety of soybean with lower rates of organic manure resulted in a finger millet yield comparable to farmers' practice, indicating that this option can be adopted by smallholder farmers who cannot produce sufficient organic manure. This study showed that plantin...

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Section: Finger Millet Performance Due To Residual Effectsmentioning

confidence: 98%

“…In 2013, the highest yield of finger millet was obtained when planted on the plot of the precursor Boshe variety, which received 3 t FYM/ha. This result indicates that during the short rainy season, the early maturing soybean type produces biomass that contributes to soil organic matter through litter fall drops (2012) and hence to soil fertility improvement [12]). Moreover, this result is in line with other research findings indicated that the lower rates of applied FYM also more easily decompose during the short rainy seasons and release some nutrients for the next crops as compared with the higher doses [11].…”

Section: Finger Millet Performance Due To Residual Effectsmentioning

confidence: 98%

See 1 more Smart Citation

The Effect of Organic and Inorganic Fertilizers on the Yield of Two Contrasting Soybean Varieties and Residual Nutrient Effects on a Subsequent Finger Millet Crop

Abebe

Deressa²

2017

Agronomy

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“…Unfortunately, tillage method for each field-year observation was not available in the NRD database used in the present study. Producer-reported yield and applied input data have been compared with other independently derived datasets and found to be in good agreement (Grassini et al, 2014b, Sadras et al, 2014, Grassini et al, 2015. Data were available from 2004 to 2013, but only those fields for which eight or more years of maize yield had been reported were retained for our analysis.…”

Section: Core Ideasmentioning

confidence: 99%

Rotation Impact on On‐Farm Yield and Input‐Use Efficiency in High‐Yield Irrigated Maize–Soybean Systems

et al. 2016

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Cereal yields tend to be higher in cereal–legume rotations relative to cereal monoculture yields. We investigated the influence of crop rotation on yield and input‐use efficiency in high‐yield irrigated maize (Zea mays L.)‐based cropping systems using producer‐reported data from western U.S. Corn Belt (about 11,000 observations). Across regions, average yield of maize grown after soybean [Glycine max (L.) Merr.] (S–M) was 0.2 to 0.6 Mg ha−1 (2–5%) higher, relative to yield of maize grown after maize (M–M). Soybean yield was 5% greater after two consecutive maize crops (M–M–S) than after only 1 yr of maize (S–M–S). Nitrogen fertilizer rate in maize fields was 13 kg N ha−1 (6%) lower in S–M than M–M fields, which, together with higher maize yields in S–M fields, resulted in 11% higher nitrogen partial factor productivity (PFPN). Difference in PFPN was unrelated with residual soil N–NO3− from prior crop. Analysis of rotation data indicated that rotation effect persists across a wide range of maize yields, from 6 to 15 Mg ha−1, though magnitude of rotation effect decreases with increasing yield level. Trends toward greater proportion of total maize area in S–M, rather than M–M, accounts for 8% of maize yield gain in U.S. Corn Belt since 1970. Similarity between our findings and previous research highlights the opportunity to quantify impact of management on yield and efficiencies by using producer data as a complement to high‐cost multi‐year, multi‐site field experiments.Core Ideas We assessed rotation effect on on‐farm yield and input‐use efficiency. Analysis was based on a large producer‐reported database collected from high‐yield irrigated maize–soybean systems. There was a consistent positive rotation effect on yield and partial factor productivity for N fertilizer. Number of previous maize crops did not affect maize yield in monoculture but soybean yields were higher following multiple maize crops. Increasing maize area in rotation relative to monoculture accounts for 8% of the maize yield gain in the U.S. Corn Belt since 1970.

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“…The SP-NRD dataset includes field-specific information from 2008-2014 on crop type, irrigation pumping volumes, and irrigated area. Detailed descriptions and quality control of NRD databases can be found in Grassini et al (2014) and Farmaha et al (2016). The above datasets provide the needed meteorological forcing, model parameters, and calibration datasets for running and evaluating the suite of irrigation modeling routines described below.…”

Section: Description Of Study Area and Historical Datamentioning

confidence: 99%

A case study of field-scale maize irrigation patterns in western Nebraska: implications for water managers and recommendations for hyper-resolution land surface modeling

Gibson

Franz

Wang

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In many agricultural regions, the human use of water for irrigation is often ignored or poorly represented in land surface models (LSMs) and operational forecasts. Because irrigation increases soil moisture, feedback on the surface energy balance, rainfall recycling, and atmospheric dynamics is not represented and may lead to reduced model skill. In this work, we describe four plausible and relatively simple irrigation routines that can be coupled to the next generation of hyper-resolution LSMs operating at scales of 1 km or less. The irrigation output from the four routines (crop model, precipitation delayed, evapotranspiration replacement, and vadose zone model) is compared against a historical field-scale irrigation database (2008-2014) from a 35 km 2 study area under maize production and center pivot irrigation in western Nebraska (USA). We find that the most yield-conservative irrigation routine (crop model) produces seasonal totals of irrigation that compare well against the observed irrigation amounts across a range of wet and dry years but with a low bias of 80 mm yr −1 . The most aggressive irrigation saving routine (vadose zone model) indicates a potential irrigation savings of 120 mm yr −1 and yield losses of less than 3 % against the crop model benchmark and historical averages. The results of the various irrigation routines and associated yield penalties will be valuable for future consideration by local water managers to be informed about the potential value of irrigation saving technologies and irrigation practices. Moreover, the routines offer the hyper-resolution LSM community a range of irrigation routines to better constrain irrigation decision-making at critical temporal (daily) and spatial scales (< 1 km).

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Drivers of spatial and temporal variation in soybean yield and irrigation requirements in the western US Corn Belt

Cited by 55 publications

References 38 publications

The Effect of Organic and Inorganic Fertilizers on the Yield of Two Contrasting Soybean Varieties and Residual Nutrient Effects on a Subsequent Finger Millet Crop

The Effect of Organic and Inorganic Fertilizers on the Yield of Two Contrasting Soybean Varieties and Residual Nutrient Effects on a Subsequent Finger Millet Crop

Rotation Impact on On‐Farm Yield and Input‐Use Efficiency in High‐Yield Irrigated Maize–Soybean Systems

A case study of field-scale maize irrigation patterns in western Nebraska: implications for water managers and recommendations for hyper-resolution land surface modeling

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